Title: Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Venue: NeurIPS 2023
Accepted: yes
Keywords: computer vision

self-supervised learning

video self-supervised learning

natural distribution shift

video learning

out-of-distribution generalization
Abstract: Video self-supervised learning (VSSL) has made significant progress in recent years. However, the exact behavior and dynamics of these models under different forms of distribution shift are not yet known. In this paper, we comprehensively study the behavior of six popular self-supervised methods (v-SimCLR, v-MoCo, v-BYOL, v-SimSiam, v-DINO, v-MAE) in response to various forms of natural distribution shift, i.e., (i) context shift, (ii) viewpoint shift, (iii) actor shift, (iv) source shift, (v) generalizability to unknown classes (zero-shot), and (vi) open-set recognition. To perform this extensive study, we carefully craft a test bed consisting of 17 in-distribution and out-of-distribution benchmark pairs using available public datasets and a series of evaluation protocols to stress-test the different methods under the intended shifts. Our study uncovers a series of intriguing findings and interesting behaviors of VSSL methods. For instance, we observe that while video models generally struggle with context shifts, v-MAE and supervised learning exhibit more robustness. Moreover, our study shows that v-MAE is a strong temporal learner, whereas contrastive methods, v-SimCLR and v-MoCo, exhibit strong performances against viewpoint shifts. When studying the notion of open-set recognition, we notice a trade-off between closed-set and open-set recognition performance if the pretrained VSSL encoders are used without finetuning. We hope that our work will contribute to the development of robust video representation learning frameworks for various real-world scenarios. The project page and code are available at: https://pritamqu.github.io/OOD-VSSL.

Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts

Abstract

Video self-supervised learning (VSSL) has made significant progress in recent years. However, the exact behavior and dynamics of these models under different forms of distribution shift are not yet known. In this paper, we comprehensively study the behavior of six popular self-supervised methods ( v -SimCLR, v -MoCo, v -BYOL, v -SimSiam, v -DINO, v -MAE) in response to various forms of natural distribution shift, i.e., ( i ) context shift, ( ii ) viewpoint shift, ( iii ) actor shift, ( iv ) source shift, ( v ) generalizability to unknown classes (zero-shot), and ( vi ) open-set recognition. To perform this extensive study, we carefully craft a test bed consisting of 17 in-distribution and out-of-distribution benchmark pairs using available public datasets and a series of evaluation protocols to stress-test the different methods under the intended shifts. Our study uncovers a series of intriguing findings and interesting behaviors of VSSL methods. For instance, we observe that while video models generally struggle with context shifts, v -MAE and supervised learning exhibit more robustness. Moreover, our study shows that v -MAE is a strong temporal learner, whereas contrastive methods, v -SimCLR and v -MoCo, exhibit strong performances against viewpoint shifts. When studying the notion of open-set recognition, we notice a trade-off between closed-set and open-set recognition performance if the pretrained VSSL encoders are used without finetuning. We hope that our work will contribute to the development of robust video representation learning frameworks for various real-world scenarios. The project page and code are available at: https://pritamqu.github.io/OOD-VSSL .

1 Introduction

Self-supervised learning has achieved tremendous success in learning strong and meaningful representations in various video domains, such as action recognition [1, 2, 3, 4, 5, 6, 7], action localization [8], video summarization [9], and video captioning [10, 11, 12]. Considering the diversity and complexity of the video domain, real-world deployment of video-based intelligent systems requires to understand the model performance under distribution shifts. Distribution shifts may occur due to differences in contextual information, viewpoint, geographical location, and the presence of unknown classes with respect to the training data, among others.

Despite the vast amount of work on VSSL [13, 14, 15, 16, 17, 18, 5, 1, 19, 8, 12], a number of fundamental questions regarding the out-of-distribution behavior and dynamics of VSSL methods remain unanswered. To date, there have been no comprehensive studies of these aspects, which we attempt to address in this paper. Specifically, we pose and answer the following questions:

Q1. How do the learned spatial and temporal representations vary based on different VSSL pretraining methodologies? How robust are these representations to different distribution shifts?

Q2. Considering recent findings about the robustness of finetuning on the generalizability of large language models (LLMs) [20, 21], we pose the question: How does finetuning influence the out-of-distribution (OoD) generalization and zero-shot performance of VSSL?

Q3. How do VSSL methods perform on open-set problems (where test samples can be from classes previously unknown to the model)? And what is the relationship between performance in closed-set vs. open-set recognition?

Q4. Do different VSSL methods exhibit comparable decision-making patterns ('decision similarity') given the same training conditions? And how is this impacted by different distribution shifts?

To address these questions, we consider six different self-supervised learning algorithms, ( i ) SimCLR [22], ( ii ) MOCO-v3 [23], ( iii ) BYOL [24], ( iv ) SimSiam [25], ( v ) DINO [26], and ( vi ) MAE [27], along with fully supervised learning, and analyze their behaviors in various OoD settings. We select these methods to cover three key categories of self-supervision, namely contrastive (SimCLR, MoCo-v3), noncontrastive (BYOL, SimSiam, DINO), and generative (MAE), approaches. In particular, these methods represent fundamental approaches on which numerous variants have been built and therefore represent many existing nuanced solutions in the area [2, 3, 4, 5, 6, 7, 1, 19, 8, 12]. For distribution shifts, we study a series of different possibilities which occur in videos in real-world settings due to changes in context (e.g., real vs. mime actions) [28, 29, 30, 31, 32], viewpoint (e.g, third-person vs. ego-centric view) [33, 34, 35, 36], actor (e.g., real-world vs. synthetic) [37, 38], and data sources (e.g., different datasets) [39, 40]. Moreover, we evaluate the generalizability of the VSSL methods on unseen classes, i.e., zero-shot recognition [41, 42, 43] and open-set recognition [44, 45, 46, 47]. To

v-SUPERVISED

v-SIMCLR

v-MOCO

v-BYOL

v-SIMSIAM

v-DINO

v-MAE

Open-set

Zero-shot

Source

Actor

Viewpoint

Context

0.92

0.99

1.00

0.97

0.92

0.94

0.92

0.81

0.94

0.95

0.94

0.89

0.90

0.84

0.88

0.93

0.96

0.99

0.87

0.91

0.79

0.82

1.00

0.99

0.94

0.94

0.90

0.93

0.80

0.98

0.98

0.89

0.88

0.84

0.91

0.93

0.86

0.89

0.91

0.86

0.88

0.93

1.0

0.8

Performance indicator

perform this investigation, we design a comprehensive study consisting of 17 in-distribution and out-of-distribution (InD-OoD) dataset pairs and examine the dynamics of VSSL under different distribution shifts using a variety of evaluation protocols including linear evaluation, finetuning, unsupervised clustering, and zero-shot recognition. Moreover, for a fair comparison, the VSSL methods are pretrained in identical experimental setups. We provide a high-level overview comparing the performance of all methods across all shifts in Figure 1. To the best of our knowledge, this is the first work to study VSSL under distribution shift to this depth.

In summary, our contributions are as follows:

We present the first comprehensive and large-scale systematic investigation of real-world distribution shifts on VSSL methods. Our study encompasses 2 large-scale pretraining datasets, 7 video learning algorithms, 17 InD-OoD dataset pairs, as well as 3 toy datasets. Our thorough evaluation involves a total of 269 experiments, covering various evaluation protocols including linear evaluation, finetuning, unsupervised clustering, zero-shot recognition, and open-set recognition.

Our study uncovers a series of intriguing behaviors and relationships of various VSSL methods, shedding light on the strengths and weaknesses that often go unnoticed in InD validation. Moreover, our investigation provides a comprehensive and impartial perspective on the effectiveness of supervised vs. self-supervised pretraining.

2 Related work

The analysis of robustness is a well-established area in computer vision, with an extensive body of research dedicated to image-based models [48, 49, 50, 51, 52]. Despite the growing popularity of video models in different domains, detailed comparative studies on their robustness remains

under-explored. We come across two recent works [53, 54] that study the behavior of video models (supervised) against synthetic perturbations. An initial study [54] explores the performance of video models against spatial corruptions like noise, blur, color jittering and others. In a subsequent work, [53] extends the analysis of video models on temporal perturbations like frame reversal, jumbling, and freezing among others. In particular, our study focuses on real-world distribution shifts, which we find crucial as synthetic perturbations yield little or no consistency to the natural distribution shifts of real data [49, 48]. Moreover, none of the prior works attempts to understand the behavior of video self-supervised methods under various forms of distribution shift.

3 Preliminaries

Contrastive methods. We study two popular contrastive methods: v -SimCLR 1 and v -MoCo. These methods learn representations by maximizing the similarity of positive pairs while minimizing the similarity of negative pairs using the InfoNCE loss [22, 23, 55], where the similarity scores are calculated using L2-normalized cosine distance. The implementation of our v -MoCo is based on MoCo-v3 [23], and it uses a momentum target encoder to fetch the key embeddings for the corresponding query embeddings. The momentum encoder is progressively updated from the online encoder using an exponential moving average (EMA) technique [56]. In contrast, v -SimCLR directly uses the online encoder to compute the similarity scores from the embeddings of the given views. Additionally, v -MoCo employs a predictor head, unlike v -SimCLR.

Non-contrastive methods. We study three popular non-contrastive methods v -BYOL, v -SimSiam, and v -DINO. These methods learn meaningful representations by minimizing the distance between positive views of a given sample, without requiring any negative pairs. v -BYOL uses an architecture similar to v -MoCo, but instead of optimizing an InfoNCE loss, it minimizes the L2-normalized cosine distance of the positive views [24, 25]. v -SimSiam is an ablation variant of v -BYOL that does not use a target encoder and directly obtains the embeddings corresponding to the views from the online encoder. The setup of v -DINO is also similar to v -BYOL, but it introduces 'Centering' [26] to prevent against model collapse, instead of using a predictor head like v -BYOL and v -SimSiam.

Generative method. Finally, we also study v -MAE which aims to learn representations through reconstructions of heavily masked inputs. v -MAE employs an autoencoder architecture [27, 1, 57] where the encoder compresses the input information, which is then reconstructed by the decoder while minimizing the L2-reconstruction error [27].

We provide additional details and design specifics for each method in Appendix B.

4 Distribution Shifts

Let p y denote a probability distribution over labels. Let p x | y denote a class conditional density of input x given labels y . We draw y from p y and then x from p x | y ( x | y ) . In the case of InD, there is no distribution shift and the validation set is drawn from the exact same distribution as above. We consider two types of distribution shifts in this paper: input-based and output-based. For Input shift , we assume that the class conditional density shifts from p x | y to q x | y . To measure the OoD performance, we choose q x | y such that there is no overlap between the distributions of the unlabelled pretraining data and OoD validation sets. For Output shift , we assume that the label distribution shifts from p y to q y . Note that we are particularly interested in cases where the support set of q y might be different from that of p y , i.e., novel classes appear at test time.

We study four different input shifts, ( i ) context shift, ( ii ) viewpoint shift, ( iii ) actor shift, and ( iv ) source shift. Context shift or out-of-context refers to when the scenic background or contextual information is misleading or absent, e.g., mime actions. Humans possess a deep understanding of actions and can thus recognize actions without much context. However, vision models tend to rely heavily on contextual cues and background objects to make predictions [28, 58, 59]. As a result, models may generalize poorly when contextual information is absent or different from what the model has been trained on. Viewpoint shift refers to the change in the viewpoint or perspective of the camera that captures the scene. Some of the popular viewpoints in the video domains include third-person view, egocentric view, bird's-eye view, top-down view, and surveillance camera view,

Context shift

Actor shift (animal)

Actor + view shift (syn.+t-down)

Driving

Opening door

Reading

View shift (surv.) + low res.

View shift (ego.)

Source shift

Carrying

Holding a picture

Ride horse

Unknown action (zero-shot)

Close set

Open-set

Cut vs. hammer watermelon

Soccer

Basketball

Badminton

Hockey

1-

among a few others. In this work, we examine the generalizability of models trained on third-person views to other viewpoints including egocentric, surveillance camera, and top-down views. Further, we study generalizability under actor shift , which occurs when the 'type' of actors changes between training and test sets. These actor-type shifts can include human-to-animal shifts or synthetic-to-real shifts. We consider human actions in the real world as InD while animal actions and synthetic videos (e.g., video games, animation) are considered OoD. Lastly, we study the distribution shifts caused due to the changes in the data sources, referred to as source shift . As discussed in [39, 40], datasets of similar classes also exhibit distribution shifts due to the difference in curation strategies, annotation schemes, demographics, geographical locations, and other factors, even when none of the specific shifts mentioned earlier apply. To assess the generalizability of video models under real-world distribution shifts, we also investigate their performance when faced with multiple forms of distribution shifts occurring simultaneously. For example, we evaluate the model's ability to handle 'top-down synthetic videos' which causes both viewpoint and actor shifts concurrently. A few examples are presented in Figure 2.

We also investigate the dynamics of video models under output shifts. We perform zero-shot recognition on both regular actions and unusual or rare actions. An example of rare actions would be 'hammering watermelon' as opposed to 'cutting watermelon', as shown in Figure 2. Lastly, we evaluate performance in open-set problems where models are required to distinguish between known vs. unknown classes while correctly predicting the known ones [60, 45, 44, 46]. Deep learning models are known to struggle in such scenarios due to their tendency towards over-confident predictions. We note that existing literature has only evaluated VSSL methods under closed-set scenarios, neglecting the importance of their performance in real-world open-set settings.

5 Experiment setup

Benchmarks. We use two large-scale video action datasets, Kinetics400 [61] and Kinetics700 [62], for pretraining the video models. The results presented in the main paper use Kinetics400 for pre-training, while we provide additional results based on pretraining with Kinetics700 in Appendix D. To evaluate the video models under distribution shifts, we use a total of 12 real-world benchmarks, comprising: Mimetics10 and Mimetics50 [28] as the out-of-context validation sets; CharadesEgo [36], TinyVirat-v2 [63], and Sims4Action [64] to investigate viewpoint shifts (egocentric, surveillance camera, and top-down views, respectively); ActorShift [65] and Sims4action [64], for actor shifts (animal and synthetic domains, respectively); UCF101 [66] and HMDB51 [67] for source shift; UCF101 [66], HMDB51 [67], and RareAct [68] for zero-shot recognition; UCF101 and HMDB51 for open-set recognition while using Kinetics400 and UCF101 as closed-set. For each OoD validation set, we create an InD training and validation set to measure the change in performance. We construct the InD splits using Kinetics400 [61], Kinetics700 [62], MiT-v2 [69], and CharadesEgo [36]. Finally, we also use 3 toy datasets to conduct experiments in controlled

setups, including ToyBox [70], COIL [34], STL-10 [71]. Additional details of the benchmarks can be found in Appendix C.

Pretraining. To ensure a fair comparison between the VSSL methods, we pretrain them in identical setups with necessary adjustments in hyperparameters. Although some of the methods studied in this work are already available in the literature [3, 5, 72, 1], they follow a variety of experiment setups including different architectures (e.g., R2+1D [73], R3D [72], TSM [74], ViT [75]), inputs, and others. Therefore, they could not be directly adopted for our experiments and are instead re-implemented. Specifically, we keep the encoder, inputs, batch sizes, and maximum number of pretraining epochs the same for all the methods. Furthermore, VSSL methods are tuned based on the InD validation split, with no exposure to OoD validation sets. We use the ViT-Base [75, 76] as the encoder, with a patch size of 4 × 16 2 . Amongst the 6 VSSL methods studied in this paper, all of them use a Siamese [77] architecture other than v -MAE. Therefore, the contrastive and non-contrastive methods are fed with 2 random spatio-temporal augmented crops from the original videos, similar to [72]. For a fair comparison, the v -MAE which requires a single view as input, is fed with 3 × 32 × 112 2 inputs, while the other VSSL methods are fed with 3 × 16 × 112 2 inputs per view. Additional details for VSSL pretraining can be found in Appendix B.

Evaluation. To study input-based distribution shifts, we perform linear evaluation and finetuning using the InD training splits, followed by evaluating on both InD and OoD validation splits. We follow the standard protocol used in [72, 2, 4, 6, 78, 79, 7] for linear evaluation and finetuning. To evaluate the models on zero-shot recognition, we follow [41, 42] and jointly train video-text encoders using the Word2Vec [80] word embeddings. Lastly, we follow [45] for open-set recognition. We report top-1 accuracy for multi-class classification and mean average precision (meanAP) for multi-label multi-class classification. Moreover, for open-set problems, we report the area under the ROC curve (AUC) for distinguishing known vs. unknown, and accuracy for measuring closed-set performance. Please find more details in Appendix C.

6 Findings

Q1: Dynamics of learned spatial and temporal representations under distribution shifts

Our experiments on context shift show that video models greatly suffer in out-of-context generalization, as OoD performance significantly drops for all of the methods as presented in Table 1. Notice that v -Supervised achieves the best OoD performance under linear evaluation ( Lin. ) and v -MAE achieves the best when finetuned ( FT ), for both benchmarks. Intuitively speaking, the models need to learn strong temporal dynamics to generalize well under context shift, as the background or contextual information may be misleading or absent. v -MAE and v -Supervised show strong temporal learning capabilities as they learn time-variant representations. This is in contrast to the other (contrastive and non-contrastive) methods which encourage learning time-invariant or time-persistent representations by minimizing the embedding distance of the positive pairs sampled from two different timestamps. Additionally, our statistical analysis in Appendix D.7 confirms the higher robustness of v -Supervised and v -MAE against context shift (10 class) in both linear and finetuned setups. Moreover, in context shift with 50 classes, v -Supervised exhibits more robustness in linear evaluation, while v -MAE is more robust when finetuned.

Method

Context (10 class)

Context (50 class)

FT

FT

OoD

InD

OoD InD

OoD

InD

OoD InD

v

-Supervised

37.0

±

0

.

4

89.2

±

0

.

5

41.2

92.7

15.0

±

0

.

5

66.3

±

0

.

5

19.0 78.1

v

-SimCLR

31.4

±

1

.

5

92.5

±

0

.

2

35.3 94.4

14.9

±

0

.

5

71.7

±

0

.

5

19.1

81.8

v

-MoCo

29.7

±

0

.

4

92.2

±

0

.

6

39.0 94.2

15.0

±

0

.

2

74.0

±

0

.

2

20.5

81.8

v

-BYOL

31.6

±

0

.

7

89.3

±

0

.

2

41.2

94.6

14.4

±

0

.

2

71.8

±

0

.

2

21.1 80.8

v

-SimSiam

30.8

±

0

.

7

89.5

±

0

.

5

40.4 93.8

13.8

±

0

.

6

67.9

±

0

.

6

19.0 78.2

v

-DINO

34.8

±

0

.

8

90.4

±

0

.

1

40.4 93.6

13.0

±

0

.

3

68.7

±

0

.

3

19.0 78.8

v

-MAE

33.3

±

1

.

8

82.9

±

1

.

0

41.2 95.2

12.3

±

0

.

4

56.4

±

0

.

4

26.0

81.4

v-BYOL

v-DINO

v-MAE

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

10.0

20.0

30.0

40.0

50.0

v-BYOL

v-DINO

v-MAE

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

20.0

30.0

40.0

50.0

60.0

70.0

(a) Transformation recog.

(b) Object recog.

To further investigate the performance of the video models in learning temporal dynamics, we conduct a toy experiment in a controlled setup using ToyBox [70]. ToyBox consists of 9 distinct temporal transformations of 12 different toy objects and transformations include positive rotations, negative rotations, and translations across the x , y , and z axes. We use the frozen VSSL encoders to extract embeddings, which are then used to perform a K-means clustering to distinguish the transformations. As the static spatial information is non-discriminative to the temporal transformations, the models have to understand the underlying temporal dynamics to correctly distinguish the applied transformations. The results, presented in Figure 3 (a), show that v -MAE and v -Supervised outperform the other methods by a large margin on this task, confirming that they are better temporal learners. Additionally, to ensure that the superiority of v -MAE and v -Supervised are not due to spatial representations, we also test the frozen encoders in object recognition using the videos of the same static objects (i.e., no temporal information), and we find that v -MAE and v -Supervised are worse among all of them (see Figure 3 (b)). This proves that v -MAE and v -Supervised are indeed strong temporal learners while being weak spatial learners.

These findings raise an interesting question: if the contrastive and non-contrastive methods are not effective temporal learners, then how do they perform well on action recognition [72, 5, 81, 2, 79, 82, 3, 7]? We believe that the strong performance of these methods on action recognition can be attributed to the significance of spatial information in classifying human activities. The temporal sequence of representations might not be as crucial for these tasks, which possibly allows contrastive and non-contrastive methods to perform well.

The results on viewpoint shifts, presented in Table 2 reveal that contrastive methods ( v -SimCLR and v -MoCo) generally achieve superior performance in all three setups in both linear and finetuning schemes. Moreover, based on the statistical analysis in Appendix D.7, v -SimCLR exhibits more robustness to all three viewpoint shifts, while v -MoCo exhibits robustness in egocentric and topdown viewpoint shifts. We believe the better performance of v -SimCLR and v -MoCo is due to the availability of negative samples during pretraining which improves viewpoint invariance compared to the other approaches. Note that while aggressive cropping also improves viewpoint invariance as discussed in [83], we do not notice such improvements in non-contrastive methods although a similar aggressive cropping is applied. It is worth noting that while v -Supervised exhibits a comparable performance to VSSL methods in egocentric and surveillance camera viewpoint shifts, its performance decreases significantly when multiple shifts are applied concurrently, as evident in the case of synthetic top-down viewpoint shift. This suggests that supervised learning may not generalize well in scenarios with more complex and realistic distribution shifts.

For further investigation, we study viewpoint invariance in a controlled setup using the pretrained VSSL encoders through unsupervised evaluation, avoiding any form of additional training which may alter the learned representations. Moreover, to limit the influence of temporal representations we use an image-based toy dataset COIL100 [34] which is commonly used to test viewpoint invariance [84, 85], as it contains images of similar objects from different angles. Our results presented in Figure 4 (left) show that v -MoCo achieves the best performance, fol-

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

65.0

70.0

75.0

80.0

85.0

112

96

64

32

16

Frame resolution

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Robustness

v-MAE

v-SUPERVISED

v-DINO

v-SIMSIAM

v-SIMCLR

v-BYOL

(a) Viewpoint invariance

(b) Low-resolution

lowed by v -SimCLR. Moreover, the results show that v -MAE is susceptible to viewpoint shifts as it performs worse. We believe that as v -MAE and v -Supervised are trained with a single view of frame sequences, they are more sensitive to viewpoint shifts. Additionally, amongst the non-contrastive methods, v -DINO shows a better performance. Notice that our results so far have revealed a trade-off between learning viewpoint invariance vs. strong temporal dynamics. While single-stream networks learn better temporal dynamics, Siamese frameworks learn better viewpoint invariance.

Notice that while contrastive methods dominate performance under viewpoint shifts (Table 2), v -MAE shows the best performance only in the case of low-resolution surveillance cameras. We hypothesize that this is due to the fact that in addition to v -MAE being a strong temporal learner (see Figure 3), it is also inherently robust to low-resolution inputs. This is likely since it learns strong pixel-level relations [86] through reconstruction of highly occluded inputs. To further investigate this, we perform a follow-up study in a controlled toy setup where we systematically decrease the resolution of input frames from 112 2 to 16 2 and measure the robustness as 1 -( acc112 -accN ) / 100 , following [53]. Here accN refers to accuracy at resolution N 2 . We use 112 2 as the maximum resolution as this is used during pretraining, and 16 2 is set as the lowest resolution as we use a patch size of 16 2 . To limit the interference of temporal representations with the outcome, we use an image dataset, STL10 [71], to perform unsupervised image classification using the frozen pretrained VSSL encoders. The results presented in Figure 4 (right) show that v -MAE achieves a more steady performance on low-resolution inputs compared to the other methods, which is in alignment with our hypothesis.

Highlights: ( a ) Video models generally struggle in out-of-context generalization, while v -Supervised and v -MAE exhibit better performance as they are strong temporal learners. ( b ) Contrastive methods ( v -SimCLR, v -MoCo) exhibit better performance to viewpoint shifts. ( c ) v -MAE is robust against extremely low-resolution inputs. ( d ) v -Supervised shows extreme vulnerability in complex scenarios when multiple distribution shifts are applied concurrently.

Method

UCF/HMDB

HMDB/UCF

FT

FT

OoD(H) InD(U) OoD(H) InD(U) OoD(U) InD(H) OoD(U) InD(H)

v

-Supervised

45.8

±

0

.

8

92.7

±

0

.

2

56.3

96.8

50.3

±

0

.

9

75.9

±

0

.

9

51.1

84.6

v

-SimCLR

47.7

±

0

.

3

96.0

±

0

.

4

53.7

97.7

55.1

±

0

.

4

82.0

±

0

.

4

56.7

85.6

v

-MoCo

51.5

±

0

.

6

97.1

±

0

.

5

55.7

98.9

57.2

±

0

.

4

84.5

±

0

.

4

58.3

86.3

v

-BYOL

51.4

±

0

.

7

94.5

±

0

.

2

59.4

97.5

63.1

±

0

.

2

79.9

±

0

.

2

60.4

87.3

v

-SimSiam

46.1

±

1

.

0

92.6

±

0

.

7

50.6

95.5

52.2

±

0

.

3

76.2

±

0

.

3

53.2

83.0

v

-DINO

49.3

±

0

.

4

94.3

±

0

.

5

51.4

96.3

53.8

±

0

.

4

77.5

±

0

.

4

55.6

82.8

v

-MAE

39.5

±

0

.

2

89.2

±

0

.

9

55.3

96.5

39.1

±

1

.

4

72.8

±

1

.

4

43.6

81.3

Q2: Effect of finetuning under distribution shifts

Our thorough empirical experiments presented in Tables 1, 2, and 3, show several interesting aspects of finetuning under distribution shifts. First, finetuning generally results in better InD and OoD performance compared to linear evaluation (see Table 5). Moreover, the benefits of finetuning vary between different VSSL methods; for example, v -MAE benefits the most from finetuning (see Figures 5 and 6). Second, the benefits of finetuning depend on the nature of the distribution shift. Specifically, by comparing OoD and InD performance, we observe that finetuning is more helpful for actor shift in both animal and synthetic domains (see Figure 5), whereas it is less beneficial for zero-shot and viewpoint shift in both egocentric and surveillance camera view. Moreover, finetuning often leads to 'in-distribution overfitting' where the InD validation improves with additional training, while leading to poor OoD

generalization (see Appendix D). Overall, the results of zero-shot recognition presented in Table 4 show that no single method dominates in all three benchmarks for zero-shot recognition. v -MAE, v -MoCo, and v -SimSiam achieve the best performances on HMDB, UCF, and RareAct, respectively, when finetuned. Third, the benefits of finetuning also depend on the InD training benchmark. For example, as shown in Figure 5, while finetuning is significantly beneficial under the source shift of UCF to HMDB, it interestingly hurts the performance when this shift is reversed (HMDB to UCF). This can be attributed to the limited utility of small-scale datasets, such as HMDB, in learning strong

representations. Such datasets could potentially even diminish the effectiveness of robust pretrained models. For instance, in the case of v -BYOL, the performance of the frozen encoder exceeds that of the finetuned model, as seen in Table 3 (HMDB/UCF).

v-Supervised Actor shift (animal)

v-SimCLR Actor shift (animal)

v-MOCO Actor shift (animal)

v-BYOL Actor shift (animal)

v-SimSiam Actor shift (animal)

v-DINO Actor shift (animal)

v-MAE Actor shift (animal)

v-Supervised Actor shift (synthetic)

v-SimCLR Actor shift (synthetic)

v-MOCO Actor shift (synthetic)

v-BYOL Actor shift (synthetic)

v-SimSiam Actor shift (synthetic)

v-DINO Actor shift (synthetic)

v-MAE Actor shift (synthetic)

v-Supervised Source shift (U/H)

v-SimCLR Source shift (U/H)

v-MOCO Source shift (U/H)

v-BYOL Source shift (U/H)

v-DINO Source shift (U/H)

v-MAE Source shift (U/H)

v-Supervised Source shift (H/U)

v-SimCLR Source shift (H/U)

v-MOCO Source shift (H/U)

v-BYOL Source shift (H/U)

v-DINO Source shift (H/U)

v-MAE Source shift (H/U)

v-Supervised View shift (ego.)

v-SimCLR View shift (ego.)

v-MOCO View shift (ego.)

v-BYOL View shift (ego.)

v-DINO View shift (ego.)

v-MAE View shift (ego.)

v-Supervised View shift (surv.)

v-SimCLR View shift (surv.)

v-MOCO View shift (surv.)

v-BYOL View shift (surv.)

v-DINO View shift (surv.)

v-MAE View shift (surv.)

v-SimCLR Zero-shot (HMDB)

v-MOCO Zero-shot (HMDB)

v-BYOL Zero-shot (HMDB)

v-DINO Zero-shot (HMDB)

v-MAE Zero-shot (HMDB)

v-SimCLR Zero-shot (RareAct)

v-MOCO Zero-shot (RareAct)

v-BYOL Zero-shot (RareAct)

v-DINO Zero-shot (RareAct)

v-MAE Zero-shot (RareAct)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Improvement (%)

In distribution

Out-of-distribution

To further investigate scenarios where finetuning may impair VSSL pretraining, we evaluate the video models on temporally perturbed inputs (e.g., freeze frames, random sampling, reverse sampling) in a controlled setup. As the VSSLmethods are never trained with temporally perturbed inputs, this constitutes a distribution shift. The results presented in Figure 6 show that end-to-end finetuning of the contrastive and noncontrastive video encoders diminishes the timeinvariance of the learned representations, which decreases robustness to temporal perturbations. As a result, in such cases, the frozen encoders perform better than the finetuned ones. Moreover, the frozen v -MAE shows the worst performance under temporal shifts as it learns timevariant representations through self-supervised pretraining.

20.0

30.0

40.0

50.0

60.0

70.0

Linear accuracy

20.0

30.0

40.0

50.0

60.0

70.0

Finetune accuracy

y=x

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

freeze

reverse sampling

random sampling

Highlights: ( a ) As opposed to LLMs, finetuning generally helps VSSL in both InD and OoD. ( b ) The benefits of finetuning largely vary between different VSSL methods and the type of distribution shifts. ( c ) Finetuning provides more benefits against actor shifts in both animal and synthetic domains in comparison to viewpoint shifts like egocentric and surveillance camera views. ( d ) Finetuning degrades robustness to temporal perturbations as it impairs the time-invariant representations of contrastive and non-contrastive methods. It can also degrade performance under source shift depending on the quality of the training benchmark.

Q3: Closed-set vs. open-set performance

We perform open-set recognition in two setups: (1) we use the Kinetics400 for closed-set recognition, while the non-overlapping classes from the UCF101 and HMDB51 are used for open-set. (2) we use UCF101 and the non-overlapping classes of HMDB for closed-set and open-set recognition, respectively. To perform a comprehensive analysis on the performance of closed-set vs. openset, we adopt two approaches, i.e, single-objective (closed-set only with cross-entropy error) and joint-objective (closed-set and open-set recognition simultaneously with DEAR loss [45]) variants.

(a.) End-to-end finetuned.

(b.) Linear evaluation.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Openness (%)

72.0

74.0

76.0

78.0

80.0

82.0

84.0

86.0

88.0

macro F1 (%)

v-MOCO

v-SIMCLR

v-BYOL

v-DINO

v-SUPERVISED

v-SIMSIAM

v-MAE

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Openness (%)

59.0

60.0

61.0

62.0

63.0

64.0

65.0

66.0

macro F1 (%)

v-MOCO

v-SIMCLR

v-BYOL

v-MAE

v-DINO

v-SIMSIAM

0.0

0.2

0.5

0.8

1.0

1.2

1.5

1.8

Openness (%)

59.0

60.0

61.0

62.0

63.0

64.0

65.0

66.0

macro F1 (%)

v-MOCO

v-SIMCLR

v-BYOL

v-MAE

v-DINO

v-SIMSIAM

75.0

77.5

80.0

82.5

85.0

87.5

Generalizability / Linear eval (Acc.)

40

45

50

55

60

65

70

75

80

Open-set recognition (AUC)

v-SUPERVISED

v-SIMCLR

v-MOCO

v-BYOL

v-SIMSIAM

v-DINO

v-MAE

Open-set chance level

(a) UCF/HMDB

(b) Kinetics400/UCF

(c) Kinetics400/HMDB

(d) Relationship b/w

closed-set and open-set performance.

The results in Table 6 (a) demonstrate that the models struggle in open-set recognition when using Kinetics400 as closed-set in comparison to UCF101. This is due to the large number of known classes in Kinetics400 compared to the unknowns ( 400 known classes in Kinetics400 vs. 31 and 22 unknown classes from UCF and HMDB, respectively). Therefore, the models tend to become over-confident and make false predictions. Evident from Table 6 (a), contrastive methods are robust to open-set recognition in all setups. This is likely due to the auxiliary information contributed by the negative samples used in these methods. To verify this hypothesis, we analyze the open macro-F1 scores vs. openness of the video models following [45, 44] by incrementally adding more unknown classes. The results in Figure 7 (a-c) show that both v -SimCLR and v -MoCo consistently achieve better performances compared to others. An additional statistical analysis on the performance of contrastive methods in open-set recognition is presented in Appendix D.7, which confirms their better performance relative to other methods.

Next, instead of end-to-end finetuning, we use the frozen encoders to train a linear head for open-set recognition. The top 3 closed-set performers, v -BYOL, v -MoCo, and v -SimCLR (see Figure 7 (d) or U101 ∗ in Table 6 (b)), show the worst performance in open-set recognition. However, the pretrained encoders, v -SimSiam, v -DINO, and v -Supervised, which are considered weaker in closedset, perform better in open-set recognition, which might be due to their lack of over-confidence. v -DINO outperforms other methods in open-set recognition achieving 79 . 4% while the performance of v -SimCLR and v -MoCo drops to almost chance levels. In comparison, v -MoCo reports 84 . 9% in standard closed-set recognition, whereas v -DINO achieves 80 . 9% . The results presented in Figure 7 (d) suggest that there is a trade-off between closed-set and open-set recognition performances, particularly when frozen encoders are used without finetuning.

Highlights: ( a ) Contrastive methods demonstrate superior performance in open-set recognition when finetuned. ( b ) There is a trade-off between closed-set and open-set recognition performance when frozen pretrained encoders are used. ( c ) Strong frozen encoders ( v -MoCo, v -SimCLR) have no open-set generalization performance due to their over-confident predictions. ( d ) On the other hand, slightly weak VSSL frozen encoders ( v -DINO, v -SimSiam) show better open-set performance, while v -MAE seems to perform poorly in both settings.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Context shift (10 cls)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.95

0.95

0.93

0.95

0.94

0.95

0.96

0.94

0.95

0.95

0.95

0.95

0.94

0.95

0.96

0.94

0.95

0.94

0.94

0.95

0.94

In-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Context shift (50 cls)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.86

0.86

0.85

0.88

0.86

0.87

0.88

0.86

0.86

0.89

0.86

0.85

0.85

0.85

0.86

0.84

0.84

0.83

0.84

0.84

0.84

In-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Actor shift (animal)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.96

0.95

0.95

0.96

0.97

0.95

0.96

0.97

0.95

0.97

0.96

0.96

0.95

0.96

0.96

0.94

0.94

0.94

0.94

0.94

0.94

In-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Actor+view shift (syn.+t-down)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.81

0.79

0.75

0.82

0.81

0.78

0.8

0.78

0.79

0.81

0.84

0.8

0.79

0.81

0.79

0.8

0.77

0.75

0.8

0.78

0.78

In-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Source shift (U/H)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.95

0.95

0.92

0.97

0.95

0.97

0.94

0.93

0.95

0.96

0.94

0.93

0.95

0.95

0.94

0.94

0.93

0.94

0.95

0.93

0.94

In-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Source shift (H/U)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.87

0.78

0.78

0.87

0.84

0.79

0.85

0.84

0.79

0.87

0.84

0.84

0.78

0.83

0.83

0.81

0.8

0.78

0.83

0.82

0.8

In-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Context shift (10 cls)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.69

0.59

0.53

0.73

0.71

0.58

0.73

0.68

0.59

0.76

0.74

0.71

0.59

0.79

0.71

0.59

0.6

0.62

0.6

0.63

0.62

Out-of-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Context shift (50 cls)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.53

0.46

0.43

0.54

0.52

0.48

0.55

0.53

0.47

0.62

0.5

0.6

0.46

0.56

0.57

0.42

0.45

0.44

0.43

0.43

0.48

Out-of-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Actor shift (animal)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.92

0.86

0.86

0.89

0.91

0.87

0.92

0.94

0.87

0.93

0.91

0.88

0.84

0.9

0.91

0.93

0.95

0.87

0.93

0.96

0.92

Out-of-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Actor+view shift (syn.+t-down)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.6

0.6

0.54

0.67

0.6

0.62

0.63

0.57

0.59

0.7

0.62

0.6

0.57

0.64

0.58

0.53

0.6

0.53

0.57

0.52

0.56

Out-of-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Source shift (U/H)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.67

0.63

0.59

0.7

0.67

0.64

0.65

0.66

0.64

0.74

0.67

0.65

0.58

0.65

0.67

0.66

0.63

0.63

0.65

0.64

0.68

Out-of-distribution

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

Source shift (H/U)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0.75

0.58

0.58

0.7

0.73

0.6

0.73

0.76

0.61

0.77

0.72

0.76

0.6

0.71

0.76

0.64

0.67

0.58

0.67

0.68

0.68

Out-of-distribution

Q4: Decision similarity under distribution shifts

To measure 'decision similarity' between the video models, we evaluate whether the models make similar predictions, regardless of their correctness. The results presented in Figure 8 demonstrate that decision similarity varies between the VSSL methods both InD and OoD, but is generally lower in OoD settings. Specifically, while we observe only a slight decrease in decision similarity in the case of animal domain actor shift, we observe significant drops in decision similarity in the case of context shifts and source shifts. Moreover, the results reveal that the decision similarity between supervised and self-supervised methods significantly drops under distribution shifts, indicating that under such shifts, VSSL methods make considerably different decisions than supervised learning. We also observe low decision similarity between v -MAE and other VSSL methods, which can be due to the generative nature of v -MAE vs. the others. Lastly, we note that contrastive methods tend to have greater decision similarities with each other than the similarities observed among non-contrastive methods. Additional results are presented in Appendix D.

Highlights: ( a ) The decision similarity of video models decreases under distribution shifts, which further varies based on the type of shift. ( b ) Context and source shifts cause the most dissimilarity between decisions. ( c ) Overall, the predictions between the supervised and self-supervised methods, as well as between v -MAE and other VSSL methods exhibit the least similarity.

7 Discussion and Summary

Limitations. We consider contrastive, non-contrastive, and generative VSSL methods in our work, as they are well-established and have demonstrated strong performance in previous studies [72, 1, 3, 5] on various video benchmarks [1, 87, 72]. However, there exists another category of VSSL methods which uses pretext tasks for self-supervision, e.g., rotation prediction [88], frame and clip order prediction [14, 89], motion prediction [90], and others [91, 92, 93, 94]. While these methods are not included in our study, they are worth exploring in future research. Moreover, our work primarily focuses on various self-supervised methods as opposed to network architectures. It would be valuable to further investigate VSSL under distribution shifts with larger Transformer [95] or convolutional [96] architectures , which we could not perform due to resource constraints. Nonetheless, our findings serve as a foundation for future studies. We discuss the broader impact of our work in Appendix A.

Summary. In this work, we thoroughly examine the behavior of VSSL methods under real-world distribution shifts that commonly occur in videos due to changes in context, viewpoint, actor, and source. Moreover, our study delves into investigating the generalizability of VSSL methods in zeroshot and open-set recognition. To rigorously evaluate the robustness of video models, we introduce a comprehensive OoD test bed curated from existing literature. This test bed is carefully designed to stress test the robustness of video models and provide a comprehensive evaluation of their capabilities. Our study uncovers a wide range of interesting dynamics of various VSSL methods under different distribution shifts, which can be instrumental in guiding future research and algorithm development in video representation learning. To the best of our knowledge, this is the first work to systematically investigate VSSL under real-world distribution shifts.

Acknowledgments and Disclosure of Funding

PS and AE are grateful to the Bank of Montreal and Mitacs for funding their research. PS and AE are also thankful to SciNet HPC Consortium for helping with the computation resources. We thank Vandad Davoodnia (Queen's University) and Ananth Balashankar (Google Research) for fruitful discussions and feedback.

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Appendix

The organization of the supplementary material is as follows:

D.8 Exploring performance variation of VSSL methods across action classes

A Broader impact

In this work, we conduct a thorough study on the behavior of Video Self-supervised Learning (VSSL) methods in the face of distribution shifts. We believe this work has far-reaching implications for advancing the field of video representation learning and its real-world applications. This work is of utmost importance primarily for the following two reasons.

Real-world deployment: Video models have a wide range of real-world applications across various domains. These applications include surveillance and security, autonomous vehicles, sports analysis, video content recommendation, human-computer interaction, video captioning and summarization, among many others. However, in real-world scenarios, distribution shifts pose a significant challenge, where the distribution of the data that the system encounters may differ from the training data distribution. Understanding how VSSL algorithms behave under real-world distributional shifts is crucial for ensuring their reliable performance and generalization capabilities for real-world applications. By studying VSSL methods under distribution shifts, we identify the limitations and challenges faced by the popular VSSL methods. We believe our work is a step towards enabling the development of robust and reliable solutions for real-world deployment.

Algorithmic robustness: Our study provides valuable insights into the algorithmic robustness of VSSL methods, advancing our understanding of VSSL algorithms. By systematically exploring the behavior of these methods under distribution shifts, we have uncovered intriguing findings and observed interesting behaviors which are previously unknown. Our work not only highlights the strengths of VSSL methods but also identifies their weaknesses in different scenarios. We find that different VSSL methods exhibit varying levels of robustness when confronted with different types of distribution shifts. Some methods demonstrate resilience against certain shifts, while they prove to be vulnerable to others. These findings pave the way for future research to develop robust frameworks that leverage the strengths of different approaches, thereby enhancing the overall performance of VSSL algorithms.

B Video self-supervised learning

B.1 Methods

In the following section, we provide a comprehensive overview of our implementation of the VSSL methods explored in this study. We define an encoder f ( · ) , which is composed of a Transformer backbone θ ( · ) and an MLP projection head h ( · ) . We further define the auxiliary network components, a target encoder f t ( · ) (utilized in v -MoCo, v -BYOL, and v -DINO), a decoder f d ( · ) (employed in v -MAE), and a predictor head f p ( · ) (utilized in v -BYOL, v -SimSiam, and v -MoCo). Note, f t ( · ) and f d ( · ) are built on the Transformer architecture, while f p ( · ) utilizes an MLP head. For given samples v , we generate two augmented views as v 1 and v 2 , where v 1 and v 2 are randomly sampled from different timestamps and differently augmented. Moreover, an augmented view v i ∈ R T × H × W × C can be expressed into a sequence of P spatio-temporal patches of size t × h × w × C , where, P = T/t × H/h × W/w . We project the spatio-temporal patches to a linear layer followed by feeding to the encoders. Following, we briefly summarize the training algorithms of these VSSL methods along with their design differences. An overview of these frameworks is presented in Figure S1.

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v -SimCLR. The objective of v -SimCLR is to maximize the similarity between positive pairs and minimize the similarity between negative pairs using the InfoNCE loss function. Given two augmented views v 1 and v 2 , we obtain z 1 and z 2 as f ( v 1 ) and f ( v 2 ) . The L2 normalized cosine similarity scores, referred to as logits , are then calculated as sim( z 1 , z 2 ) , where sim( z 1 , z 2 ) = z ⊤ 1 z 2 || z 1 |||| z 2 || . The pretraining loss is calculated as L simclr = InfoNCE(logits) as per Equation S1, where k + refers to the positive embeddings (i.e., embeddings of the augmented views) and k -refers to the negative embeddings (embeddings of other samples in a minibatch).

v -MoCo. Similar to v -SimCLR, v -MoCo also calculates InfoNCE loss for given query-key pairs. We obtain the query embeddings as p 1 = f p ( f ( v 1 )) and its corresponding key embeddings are obtained as zz 2 = sg( f t ( v 2 )) , where sg refers to stop-gradient operation. In Equation S1, the positive and negative key embeddings { k + , k -} ∈ zz 2 and the query embedding q ∈ p 1 . To calculate a symmetrized loss we also obtain p 2 and zz 1 . The training objective L moco is defined as InfoNCE(sim( p 1 , zz 2 )) + InfoNCE(sim( p 2 , zz 1 )) . The encoder f is updated using L moco , and the target encoder f t is updated using the exponential moving average (EMA) [56] as f t ← αf +(1 -α ) f , where α is the EMA coefficient.

v -BYOL. Different from v -SimCLR and v -MoCo, v -BYOL does not require any negative pairs and only relies on positive views. Therefore, we obtain p 1 and zz 2 as f p ( f ( v 1 )) and sg( f t ( v 2 )) to calculate the similarity scores as sim( p 1 , zz 2 ) . Moreover, to obtain a symmetrized loss, we also calculate sim( p 2 , zz 1 ) and define total loss L byol = -sim( p 1 , zz 2 ) -sim( p 2 , zz 1 ) . Please note, we use L byol to train the f , and the weights of f t are updated using an EMA similar to v -MoCo.

v -SimSiam. The approach for v -SimSiam is very similar to the v -BYOL, except it does not use f t , and directly uses the representations of the f as the target. We obtain p 1 and z 2 as f p ( f ( v 1 )) and sg( f ( v 2 )) to calculate the similarity scores as sim( p 1 , z 2 ) . Similarly, we also obtain sim( p 2 , z 1 ) and define total loss L simsiam = -sim( p 1 , z 2 ) -sim( p 2 , z 1 ) .

v -DINO. Similar to v -BYOL and v -MoCo, v -DINO uses a target encoder f t ( · ) but does not use a predictor head. We obtain z 1 and zz 2 as f ( v 1 ) and sg( f t ( v 2 ) -C ) respectively, where C refers to the Centering operation which can be interpreted as adding a bias term, preventing the model from collapsing [26]. Next, we calculate the cross-entropy ( CE ) error between z 1 and zz 2 as CE( z 1 , zz 2 ) = -softmax( zz 2 /τ t ) log(softmax( z 1 /τ s )) , where τ t and τ s denote the temperatures used to sharpen the representations of the target and base encoder respectively. Finally, we calculate loss L dino = CE( z 1 + zz 2 ) + CE( z 2 , zz 1 ) which is used to update f and the weights of f t are updated using EMA similar to v -BYOL and v -MoCo.

v -MAE. Unlike the methods mentioned above, v -MAE takes a single view and performs masking and reconstruction using an autoencoder architecture [27, 1, 57]. Moreover, different from the other models, the encoder does not contain a projection head, i.e., f = θ , instead uses a decoder f d for reconstruction. For a given sample v , we obtain masked tokens v [ m ] as { v i | m i = 1 } P i =1 and the corrupted inputs as ˆ v = { v | m i = 0 } P i =1 . We calculate the reconstruction loss as L mae = ( f d ( θ (ˆ v )) -v [ m ] ) 2 , which is used to train the θ and f d .

After pretraining using the aforementioned methods, we discard the auxiliary network components and utilize only θ for downstream evaluations.

B.2 Implementation details

Datasets. We use 2 popular large-scale datasets Kinetics400 [61] and Kinetics700 [62] for pretraining. Kinetics400 consists of 240 K samples including 400 action classes, whereas, Kinetics700 consists of 537 Ktraining samples spread over 700 action classes. Following [97, 42], we discard the overlapping classes between Kintics700 and UCF101 [66], HMDB51 [67] from pretraining, which results in approximately 480 K samples spread over 663 action classes. We follow such a setup so that the pretrained encoder can be used for zero-shot recognition on full UCF101 and HMDB51.

Pretraining. To ensure a fair comparison between the VSSL methods, we pretrain them in identical setups with necessary adjustments in hyperparameters. Specifically, we keep the encoder, inputs, batch size, and maximum number of pretraining epochs identical for all the methods. However, we adjust the data augmentation strategy and auxiliary network components (e.g., predictor head, projector head, decoder) as required to achieve their best performance on 2 validation sets Kinetics400 and UCF101. For example, the projector head of v -DINO is based on the configuration proposed in [26], whereas, the projector head for the other Siamese frameworks is made of an MLP head similar to [24, 25]. The details of the frameworks are presented in Table S1. Considering the widespread use of Transformers in vision, VSSL methods are implemented using ViT-B [75] as the encoder with a patch size of 4 × 16 2 . Videos are downsampled to 8 FPS and resized to 3 × 112 2 frames. Except for v -MAE, all VSSL methods are fed with 16 frames in each view and 32 frames are fed as input to v -MAE. We use AdamW [98, 99] optimizer with cosine learning rate scheduler and train all methods up to 800 epochs with a batch size of 768 . We present the hyperparameters in Table S3. All the methods are pretrained using 8 V100 32 GB GPUs in parallel. We present the computational specifics

in Table S2, which shows that v -MAE is the most computationally efficient framework amongst all the VSSL methods.

B.3 Additional insights

Aggressive cropping. As discussed in the main paper, aggressive cropping leads to viewpoint invariance, therefore we experiment with various multi-crop ratios commonly used in the literature [22, 72] across all six VSSL methods. We present the best setups corresponding to individual methods in Table S3. We observe that aggressive cropping negatively impacts the performance of v -MAE. This is primarily because reconstructing a local crop serves as a weak pseudo task due to limited pixel-level variation, resulting in suboptimal representation learning. Additionally, as depicted in Figure S2, the impact of cropping varies between contrastive and non-contrastive methods. Very high aggressive cropping ratios ( 0 . 08 -1 . 0 ) prove beneficial for contrastive methods, while slightly less aggressive cropping ratios ( 0 . 2 -0 . 766 ) yield better results for non-contrastive methods.

Pretraining epoch vs. accuracy. We track the performance of different videl learning methods on two downstream benchmarks, Kinetics400 and split-1 of UCF101. In particular, Kinetics400 is used for finetuning and UCF101 for linear evaluation. The results are presented in Figure S3 shows that while v -MAE achieves the lowest linear evaluation accuracy, it shows the best performance when finetuned. Moreover, v -BYOL, v -SimCLR, and v -MoCo exhibit superior performance compared to v -DINO and v -SimSiam in both the linear evaluation and finetuning setups. Interestingly, contrastive

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(b) Kinetics400 (finetuned)

Spatial vs. temporal dependency. In addition to the out-distribution tests discussed in the main paper, we also conduct several interesting analyses to understand the dependency of these VSSL

No Temporal

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methods towards spatial vs. temporal representations. To explore the spatial vs. temporal dependency of the VSSL methods, we use UCF101 as the base validation dataset and create 3 synthetic variant of it, ( i ) No Temporal : we completely remove the temporal information from the input and treat single frames as an input. In particular, we obtain 80 frames per video that represent 10 seconds of video sampled at 8 FPS. ( ii ) Partial Appearance : Next, to suppress the spatial information, we obtain the optical flow [100] from the original videos. ( iii ) No Appearance : Following [101], we also obtain the videos of no appearance, i.e., the static frames have no meaning and actions are present in the temporal dimension. We present sample frames of these variants in Figure S4.

The results are presented in Table S4 show several intriguing observations. First, removing the temporal dimension from the videos leads to a significant drop in performance in all the methods. Amongst all the video models, v -MAE suffers the worst in the absence of temporal information in both linear and finetune setups, which shows its overly dependent nature on temporal information and it is a weak spatial learner (also discussed in the main paper). Second, in the absence of temporal information, the frozen encoder outperforms the finetuned variant even in the case of v -Supervised. We also notice the performance of the non-contrastive methods significantly varies under such setups, for example, while v -BYOL achieves the best performance in linear evaluation, v -DINO performs worse other than v -MAE. Additionally, our tests under partial or no appearance reveal that none of the video models is capable of understanding actions just from the temporal information. In fact, the performance of video models drops near chance level ( 1% ) in no appearance setup and achieve slightly better ( 3 -7% ) in partial appearance setup.

C Distribution Shifts

C.1 Overview

We provide a summary of our proposed out-of-distribution test bed in Table S5, and the details of our setup are described in the following subsections. Due to the large number of experiments conducted across various setups, we will release configuration files for individual evaluation on the project website rather than sharing them here.

C.2 Context shift

We use the two splits of Mimetics (i.e., Mimetics10 [102] and Mimetics50 [28]) as the OoD validation benchmark to study video models under context shift. Mimetics consists of a subset of action classes from Kinetics400, where the context in the videos is either partial or misleading or completely absent. We train the pretrained encoders using videos of corresponding action classes from Kinetics400, and subsequently perform InD and OoD validations using Kinetics400 and Mimetics, respectively. For linear evaluation, we extract the fixed features by taking the mean of all the patches from the last layer of the encoder. We then employ an SVM with a linear kernel for classification. In the finetuning stage, we add a fully-connected layer on top of the encoder and train it end-to-end using the AdamW optimizer and cross-entropy error. The top-1 accuracy is reported for both InD and OoD validation. Please note, the different VSSL methods are individually tuned to achieve their best performance.

C.3 Viewpoint shift

We study three types of viewpoint shifts, ( i ) egocentric view, ( ii ) surveillance camera view, and ( iii ) top-down view. We use CharadesEgo [36], TinyVirat-v2 [63], and Sims4Action [64] as the OoD benchmark for egocentric view, low-resolution surveillance camera view, and top-down view respectively. CharadesEgo is a collection of paired third-person and first-person videos of human activities. Therefore, we use the corresponding third-person videos from CharadesEgo [36] for InD training and validation split while the egocentric view is used as OoD validation. We use MiT-v2 [69] to create the corresponding InD split for TinyVirat-v2 and Sims4Action [64]. MiT-v2 is a large-scale video benchmark of 1M videos spread over 339 different human actions. We use the videos of overlapping classes between MiT-v2 and TinyVirat-v2 to study surveillance camera viewpoint shift.

Similarly, the videos of overlapping classes between MiT-v2 and Sims4Action are used to study top-down viewpoint shifts. The class assignments between InD and OoD benchmarks are mentioned at the end of this subsection.

For training the models on CharadesEgo, we adopt the approach proposed in [103]. In this setup, instead of using one-hot encoded labels, we utilize the text descriptions associated with the videos. We generate tokens from these descriptions and train a video-text encoder using an InfoNCE objective function [55]. In particular, we use the DistilBERT base [104] as the text encoder along with the pretrained ViT-B is used as the video encoder. We redirect the readers to [103], to know more about the training scheme of CharadesEgo. In the case of linear evaluation, the above-mentioned setup remains the same, except we keep the video encoder frozen. Moreover, our training strategy with MiT-v2 to study surveillance camera viewpoint and top-down viewpoint shifts follow a standard finetuning setup using cross-entropy error [72, 2, 4, 6, 78, 79, 7]. However, due to the difference in the nature of the datasets, as TinyVirat contains multiple actions per video, whereas, MiT-v2 consists of single actions per video, we make an adjustment in the prediction layer. Instead of using a Softmax layer, we utilize a Sigmoid layer to predict multiple possible outputs for a given input video. We set the threshold for classification at 0.5. Similarly, the setup for linear evaluation on TinyVirat remains the same, except the video encoder is kept frozen. Next, in the case of Sims4Action, we use SVM for linear evaluation. We report mean average precision and F1-score for CharadesEgo and TinyVirat, respectively, and top-1 accuracy for the rest of them.

TinyVirat-v2 and MiT-v2. opening: opening, pull: pulling, activity_carrying: carrying, entering: entering, exiting: exiting, loading: loading, talking: talking, activity_running: running, riding: riding, closing: closing, activity_walking: walking, push: pushing, activity_standing: standing, specialized_talking_phone: telephoning.

Sims4Action and MiT-v2. cook: cooking, drink: drinking, eat: eating, read_book: reading, use_phone: telephoning, walk: walking.

C.4 Actor shift

We study actor shift in two setups, i.e., animal domain and synthetic domain. We use the ActorShift [65] as the OoD validation set to test generalizability on animal actions, while using the video of overlapping classes from Kinetics700 [62] as InD training and validation. We use SVM for linear evaluation and follow standard techniques [72, 2, 4, 6, 78, 79, 7] as discussed earlier for finetuning. We report top-1 accuracy for both InD and OoD validation. We present the overlapping classes between Kinetics700 and ActorShift at the end of this subsection. The setup for synthetic domain shift is already discussed in the previous subsection (Viewpoint shift).

ActorShift and Kinetics700. sleeping, watching tv, eating, drinking, swimming, running, opening door.

C.5 Source shift

To study source shift, we use the videos from the common classes of UCF101 and HMDB51. While using the UCF as the training split, we use the corresponding videos from HMDB as the OoD validation set and vice versa. Following standard protocol [72, 2, 4, 6, 78, 79, 7], we use SVM for linear evaluation and the pretrained encoder along with a fully-connected layer for finetuning. We report the top-1 accuracy for both setups. The class assignments are as follows.

UCF101 and HMDB51. FrisbeeCatch: catch, RockClimbingIndoor: climb, Diving: dive, (BasketballDunk, Basketball): dribble, Fencing: fencing, GolfSwing: golf, (HandstandPushups, HandstandWalking): handstand, (LongJump, JumpingJack): jump, SoccerPenalty: kick_ball, PullUps: pullup, Punch: punch, PushUps: pushup, Biking: ride_bike, HorseRiding: ride_horse, ThrowDiscus: throw, WalkingWithDog: walk, Archery: shoot_bow,

C.6 Zero-shot

We perform zero-shot in two setups: (1) we train the video-text encoder on Kinetics400, while the non-overlapping classes from UCF101 and HMDB51 are used for OoD validation [105]. (2) We train the models using the videos from Kinetics700 that do not share classes with UCF101 and HMDB51,

while the full UCF101 and HMDB51 are used for OoD validation. Moreover, in both cases, we also use RareAct as the OoD benchmark, which consists of rare or unusual video actions.

Since, the pretraining of VSSL methods does not employ language, first, a video-text encoder is jointly trained to learn the mapping between video and text representations. Following [42], we use Word2Vec [80] to extract the word embeddings from the labels, which are then fed to a text encoder consists of an MLP head. We measure the similarity between the video and text pairs as the dot product of L2 normalized video-text embeddings, referred to as logits. Next, the video text encoders are trained to minimize the Softmax cross-entropy error using the obtained logits and the class labels. We redirect readers to [42] for additional details of the training method. Please note, we also try with a stronger language model (e.g., BERT[106], DistilBERT[104]) than Word2Vec, however, it does not improve the performance, in fact, slightly worsens it. Since the labels in Kinetics are typically composed of only a few words (mostly less than 2), the average pooled word embeddings work well in such cases. This finding is consistent with the observations of [42]. Moreover, we perform zero-shot in both setups when the pretrained video encoder is kept frozen and finetuned in an end-to-end manner.

C.7 Open-set

We perform open-set recognition in two setups: (1) We use the Kinetics400 for closed-set recognition, while the non-overlapping classes from the UCF101 and HMDB51 are used for open-set, similar splits that have been used in zero-shot. (2) We use UCF101 and the non-overlapping classes of HMDB for closed-set and open-set recognition, respectively. We obtain the non-overlapping classes by manual inspection, presented at the end of this subsection.

To enable the models for open-set recognition, we follow [45] and train the pretrained encoders with the DEAR loss which aims to calibrate the uncertainty to ensure the models show high uncertainty towards the unknown and low uncertainty towards the known. We redirect readers to [45] for more information about open-set training. We conduct open-set recognition in both finetuned and linear evaluation setups. We observe that both supervised training and linear evaluation setups using VSSL encoders did not converge when Kinetics400 was used for closed set recognition. This is likely because the models tend to become over-confident due to the large number of known classes in Kinetics400 compared to the unknowns. To measure the models' performance we report the area under the ROC curve (AUC) for open-set recognition and accuracy for closed-set recognition.

HMDB51. The non-overlapping classes from HMDB51 used in open-set recognition while using UCF101 as the closed-set: brush_hair, cartwheel, chew, clap, climb_stairs, drink, eat, fall_floor, flic_flac, hit, hug, kick, kiss, laugh, pick, pour, push, run, shake_hands, shoot_ball, shoot_bow, shoot_gun, sit, situp, smile, smoke, somersault, stand, swing_baseball, sword, sword_exercise, talk, turn, wave.

C.8 Toy experiments

In all of our toy experiments, we use the pretrained frozen encoders to extract the fixed representations, which are then used for K-means clustering 2 . Additionally, we apply the Hungarian algorithm [107] for assigning the formed cluster to the targets and measure the accuracy. The details of the datasets used to conduct the toy experiments are as follows.

C.8.1 ToyBox

The ToyBox [108] consists of egocentric views of 9 distinct temporal transformations of different toy objects. The transformations include positive rotations, negative rotations, and translations across the x , y , and z axis. A few representative frames illustrating such transformations are presented in Figure S5. Using the pretrained frozen encoders, we aim to correctly cluster different transformations across various objects. Moreover, as the static spatial information is non-discriminative to the temporal transformations, we aim to form clusters solely based on temporal representations. The main motivation behind this experiment is to disentangle spatial representations from the embedding space to independently evaluate the models on their ability to learn temporal dynamics.

C.8.2 COIL100

We use COIL100 [34] to study viewpoint invariance which consists of images of different objects captured from different camera angles. We particularly choose an image-based dataset for this experiment to restrict any discriminatory temporal features in the embedding space that may attribute to the outcome of the models' prediction. We present a few representative frames in Figure S6

C.8.3 STL10

We use an image dataset STL10 [71] to test the robustness of the pretrained encoder against lowresolution inputs. We systematically reduce the resolution from 112 2 to 16 2 , please see a few examples in Figure S7. Similar to our viewpoint shift setup, we choose an image dataset to restrict any influence of temporal representations in the outcome.

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C.9 License of Dataset

In Table S6, we summarize the licensing terms for the datasets used in this study.

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D Additional Experiments and Results

D.1 Pretrained on Kinetics700

In addition to the Kinetics400 used in the main paper, we further utilize the Kinetics700 to evaluate the benefits of incorporating more diverse data for out-of-distribution generalization. We use a subset of Kinetics700 comprising 480K samples, which doubles the size of Kinetics400. We use this subset to pretrain the VSSL methods for the same number of total iterations as Kinetics400. Unless stated otherwise, we compare the performance between Kinetics400 and Kinetics700 pretraining in linear evaluation, averaging over 3 trials. In Table S7 and Figure S8, we present a high-level overview showing the impact of using more diverse data in pretraining. Table S7 highlights that non-contrastive methods ( v -BYOL, v -SimSiam, v -DINO) benefit more from the inclusion of diverse data compared to the contrastive approaches ( v -SimCLR, v -MoCo) and v -MAE. Interestingly, we observe a slight decrease in the performance of v -MAE in this setting. This could be attributed to several factors, such as the pretrained encoder ViT-B reaching saturation in the pretraining setup of v -MAE, resulting in no improvements in performance from the additional data. To address this, a larger backbone like ViT-L or ViT-H, as used in prior works [57, 1], could be explored. Unfortunately, resource limitations prevented us from adopting a larger network to test this. Detailed results are presented in Tables S8, S9, S10, S11 and S12.

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D.2 Temporal dynamics

In this subsection, we provide the additional results of our experiment on the temporal dynamics of VSSL methods. We present the performance on temporal transformation recognition in Figure S9. Moreover, we present the performance of VSSL methods in classifying the same objects from static videos, in Figure S10.

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D.3 Comparing InD vs. OoD performance under natural distribution shifts

To gain a high-level understanding of the models' performance in InD vs. OoD, we visualize their accuracy in a 2D space in Figure S11. It is noteworthy that superior InD performance does not necessarily translate to better OoD performance. We observe cases where models exhibit similar InD performance but significantly differ in their performance under distribution shifts. Ablation variants of Figure S11 are presented in Figure S12. Statistical analyses on the robustness of video models are presented in Appendix D.7.

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D.4 Effect of synthetic perturbations

In addition to the natural distribution shifts, we further extend our work investigating the performance of VSSL methods under synthetic perturbations. We follow the setup proposed in [53] to create the synthetic perturbation of varying severity on a scale of 1 to 5 . We apply a total of 16 different synthetic perturbations belonging to the common spatio-temporal augmentation techniques such as noise addition, blurring, temporal perturbation, and camera motion. We test the robustness of the frozen encoders against synthetic perturbations on 2 benchmarks UCF101 and Kinetics400, presented in Figures S13 and S14 respectively. The results demonstrate that with increasing perturbation severity, the performance of video models deteriorates. However, it is important to acknowledge that certain visual augmentations, such as noise and blur, are already applied during the self-supervised pretraining phase. As a result, they may not be considered as distribution shifts within the scope of our setup. Furthermore, it is neither possible to train self-supervised methods without such augmentation techniques. Despite these considerations, we present these results for the sake of completeness.

Since spatial perturbations are extensively covered in literature, we refrain from explaining them here and redirect readers to [53, 54, 49]. Instead, we briefly discuss the temporal perturbations [53]. 'Sampling': using different frame rates than used in training; 'Reverse sampling': reversing the temporal order with different sampling rates; 'Jumble': shuffling frames in segments; 'Box jumble': shuffling the segments instead of frames; 'Freezing': randomly freezing video frames; 'Random shuffle': randomly shuffle the frame orders, please note random shuffle has just one severity level.

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Speckle Noise

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

0

20

40

60

80

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

70

80

Acc.

Translate

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

70

80

Acc.

Rotate

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

20

30

40

50

60

70

80

Acc.

Static Rotate

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

55

60

65

70

75

80

85

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

70

80

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

70

80

Acc.

Defocus Blur

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

20

30

40

50

60

70

80

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

20

30

40

50

60

70

80

Acc.

Sampling

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

30

40

50

60

70

80

Acc.

Jumble

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

60

65

70

75

80

85

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

20

30

40

50

60

70

80

Acc.

Freeze

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

20

30

40

50

60

70

80

Acc.

Random Shuffle

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

0

10

20

30

40

50

60

Acc.

Impulse Noise

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

Speckle Noise

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

0

10

20

30

40

50

60

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

0

10

20

30

40

50

60

Acc.

Translate

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

0

10

20

30

40

50

60

Acc.

Rotate

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

Static Rotate

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

25

30

35

40

45

50

55

60

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

Defocus Blur

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

Sampling

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

Jumble

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

30

35

40

45

50

55

60

Acc.

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

20

30

40

50

60

Acc.

Freeze

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

0

1

2

3

4

5

Perturbation Severity

10

20

30

40

50

60

Acc.

Random Shuffle

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

D.5 Effect of finetuning in out-of-distribution generalization

D.5.1 Linear vs. finetuning performance comparison

We conduct a thorough study evaluating the impact of finetuning across different VSSL methods. We present a high-level overview in Table S14 and more detailed results in Figures S15, S16, and S17.

v-Supervised Context shift (10 cls)

v-SimCLR Context shift (10 cls)

v-MOCO Context shift (10 cls)

v-BYOL Context shift (10 cls)

v-DINO Context shift (10 cls)

v-MAE Context shift (10 cls)

v-Supervised Context shift (50 cls)

v-SimCLR Context shift (50 cls)

v-MOCO Context shift (50 cls)

v-BYOL Context shift (50 cls)

v-DINO Context shift (50 cls)

v-MAE Context shift (50 cls)

v-Supervised Actor shift (animal)

v-SimCLR Actor shift (animal)

v-MOCO Actor shift (animal)

v-BYOL Actor shift (animal)

v-SimSiam Actor shift (animal)

v-DINO Actor shift (animal)

v-MAE Actor shift (animal)

v-Supervised Actor+view shift (syn.+t-down)

v-SimCLR Actor+view shift (syn.+t-down)

v-MOCO Actor+view shift (syn.+t-down)

v-BYOL Actor+view shift (syn.+t-down)

v-SimSiam Actor+view shift (syn.+t-down)

v-DINO Actor+view shift (syn.+t-down)

v-MAE Actor+view shift (syn.+t-down)

v-Supervised Source shift (U/H)

v-SimCLR Source shift (U/H)

v-MOCO Source shift (U/H)

v-BYOL Source shift (U/H)

v-DINO Source shift (U/H)

v-MAE Source shift (U/H)

v-Supervised Source shift (H/U)

v-SimCLR Source shift (H/U)

v-MOCO Source shift (H/U)

v-BYOL Source shift (H/U)

v-DINO Source shift (H/U)

v-MAE Source shift (H/U)

v-Supervised View shift (ego.)

v-SimCLR View shift (ego.)

v-MOCO View shift (ego.)

v-BYOL View shift (ego.)

v-DINO View shift (ego.)

v-MAE View shift (ego.)

v-Supervised View shift (surv.+low res.)

v-SimCLR View shift (surv.+low res.)

v-MOCO View shift (surv.+low res.)

v-BYOL View shift (surv.+low res.)

v-DINO View shift (surv.+low res.)

v-MAE View shift (surv.+low res.)

v-SimCLR Zero-shot (UCF)

v-MOCO Zero-shot (UCF)

v-BYOL Zero-shot (UCF)

v-DINO Zero-shot (UCF)

v-MAE Zero-shot (UCF)

v-SimCLR Zero-shot (HMDB)

v-MOCO Zero-shot (HMDB)

v-BYOL Zero-shot (HMDB)

v-DINO Zero-shot (HMDB)

v-MAE Zero-shot (HMDB)

v-SimCLR Zero-shot (RareAct)

v-MOCO Zero-shot (RareAct)

v-BYOL Zero-shot (RareAct)

v-DINO Zero-shot (RareAct)

v-MAE Zero-shot (RareAct)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Improvement (%)

In distribution

Out-of-distribution

20.0

40.0

60.0

80.0

100.0

Linear accuracy

20.0

40.0

60.0

80.0

100.0

Finetune accuracy

y=x

In-distribution (filled)

Out-of-distribution (empty)

Source shift (H/U)

Actor shift (animal)

Zero-shot (HMDB)

Context shift (10 cls)

Context shift (50 cls)

Zero-shot (RareAct)

Actor+view shift (syn.+t-down)

Zero-shot (UCF)

View shift (surv.+low res.)

View shift (ego.)

Source shift (U/H)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Linear accuracy

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Linear accuracy

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

7.5

10.0

12.5

15.0

17.5

20.0

22.5

25.0

Linear accuracy

7.5

10.0

12.5

15.0

17.5

20.0

22.5

25.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

(a) Context shift (10 classes)

(b) Context shift (50 classes)

(c) Viewpoint shift (ego.)

20.0

25.0

30.0

35.0

40.0

45.0

50.0

Linear accuracy

20.0

25.0

30.0

35.0

40.0

45.0

50.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

30.0

40.0

50.0

60.0

70.0

80.0

Linear accuracy

30.0

40.0

50.0

60.0

70.0

80.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

55.0

60.0

65.0

70.0

75.0

80.0

85.0

90.0

95.0

Linear accuracy

55.0

60.0

65.0

70.0

75.0

80.0

85.0

90.0

95.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

(d) Viewpoint shift (surv.+low res.)

(e) Viewpoint+actor shift (top-down+synthetic)

(f) Actor shift (animal)

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Linear accuracy

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Linear accuracy

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

v-SUPERVISED

30.0

40.0

50.0

60.0

70.0

Linear accuracy

30.0

40.0

50.0

60.0

70.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

(g) Source shift (U/H)

(h) Source shift (H/U)

(i) Zero-shot (UCF)

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Linear accuracy

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Linear accuracy

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Finetune accuracy

y=x

y=mx+c

In-distribution (filled)

Out-of-distribution (empty)

v-BYOL

v-DINO

v-MAE

v-MOCO

v-SIMCLR

v-SIMSIAM

(j) Zero-shot (HMDB)

(k) Zero-shot (RareAct)

D.5.2 In-distribution overfitting due to finetuning

As mentioned in the main paper, while finetuning enhances overall performance, it is susceptible to in-distribution overfitting. In other words, while additional training leads to improved performance on InD validation, it adversely affects OoD performance. We present a few examples of such instances in Figure S18. In practice, we apply early stopping to tackle such overfitting.

0

20

40

60

80

100

Epochs

30

40

50

60

70

Acc.

OoD

InD

0

20

40

60

80

100

Epochs

40

50

60

70

80

Acc.

OoD

InD

0

20

40

60

80

100

Epochs

30

40

50

60

70

Acc.

OoD

InD

v

-BYOL

Actor shift (synthetic)

v

-SimCLR

Actor shift (synthetic)

v

-DINO

Actor shift (synthetic)

0

20

40

60

80

100

Epochs

55

60

65

70

75

80

85

90

Acc.

OoD

InD

0

10

20

30

40

50

Epochs

20

30

40

50

60

70

Acc.

OoD

InD

0

10

20

30

40

50

Epochs

30

40

50

60

70

Acc.

OoD

InD

v

-SimSiam

Actor shift (animal)

v

-BYOL

Zero-shot (OoD: HMDB, InD: Kinetics400)

v

-MoCo

Zero-shot (OoD: UCF, InD: Kinetics400)

D.6 High-level overview

The high-level overview presented in Figure 1 summarizes our findings from Tables 1, 2, 3, 4, and 6(a) of the main paper. To create the overview plot, we first normalize the OoD performance (e.g., accuracy, mAP) of each experiment to that of the best-performing method. Thus, the best performing method gets 1 . 0 and all other methods get a score in [0 , 1] . Next, we group the experiments based on their super-categories and then take their average score. For example, in the case of source shift, we compute this average by grouping the normalized scores of 4 experiments, namely linear and finetuning evaluation of both UCF/HMDB and HMDB/UCF shifts. We follow similar steps for all the shifts (details are given below), resulting in a 7 × 6 matrix, where 7 represents the number of video methods, and 6 represents the total number of shifts. This final matrix is then used to color-code and present a high-level summary of all methods across all shifts.

Context shift: Table 1

-Context shift (10 classes) linear; best performing method: v -Supervised

-Context shift (10 classes) finetune; best performing methods: v -Supervised, v -BYOL, v -MAE

-Context shift (50 classes) linear; best performing methods: v -Supervised, v -MoCo

-Context shift (50 classes) finetune; best performing method: v -MAE

Viewpoint shift: Table 2

-Viewpoint shift (egocentric) linear; best performing method: v -MoCo

-Viewpoint shift (egocentric) finetune; best performing method: v -MoCo

-Viewpoint shift (surveillance+low resolution) linear; best performing method: v -SimCLR

-Viewpoint shift (surveillance+low resolution) finetune; best performing method: v -MAE

-Viewpoint+actor shift (top-down+synthetic) linear; best performing method: v -SimCLR

-Viewpoint+actor shift (top-down+synthetic) finetune; best performing method: v -SimCLR

Actor shift: Table 2

-Viewpoint+actor shift (top-down+synthetic) linear; best performing method: v -SimCLR

-Viewpoint+actor shift (top-down+synthetic) finetune; best performing method: v -SimCLR

-Actor shift (animal) linear; best performing method: v -BYOL

-Actor shift (animal) finetune; best performing method: v -SimCLR

Source shift: Table 3

-Source shift (UCF/HMDB) linear; best performing method: v -MoCo

-Source shift (UCF/HMDB) finetune; best performing method: v -BYOL

-Source shift (HMDB/UCF) linear; best performing method: v -BYOL

-Source shift (HMDB/UCF) finetune; best performing method: v -BYOL

Zero-shot recognition: Table 4

-Zero-shot (UCF) linear; best performing method: v -SimCLR

-Zero-shot (UCF) finetune; best performing method: v -MoCo

-Zero-shot (HMDB) linear; best performing method: v -BYOL

-Zero-shot (HMDB) finetune; best performing method: v -MAE

-Zero-shot (RareAct) linear; best performing method: v -MoCo

-Zero-shot (RareAct) finetune; best performing method: v -SimSiam

Open-set recognition: Table 6(a)

-Open-set (K400/UCF) finetune; best performing method: v -SimCLR

-Open-set (K400/HMDB) finetune; best performing method: v -MoCo

-Open-set (U101/HHDB) finetune; best performing method: v -MoCo

D.7 Statistical analysis

We present detailed statistical analyses to verify the statistical significance of all the major findings/claims discussed in the main paper. In particular, we compare the robustness of the video models under different distribution shifts, after compensating their InD performance. The adjusted OoD performance difference between the two methods is calculated as:

Here, m ∈ [0 , 1] is the slope of a linear fit of projected InD and OoD performance measures of all the models for a particular shift, calculated as:

where x i and y i refer to InD and OoD performance measures for i th method and i ∈ [1 , n ] , and n is the total number of methods studied. Moreover, ¯ x and ¯ y refer to the mean InD and OoD performances, respectively. Between Method 1 and Method 2, we calculate ∆ OoD = y 1 -y 2 and ∆ InD = x 1 -x 2 , while SE OoD is the standard error of Method 1's OoD performance across several trials. We consider the standard error with statistical significance at 95% confidence. Finally, we accept Method 1 is more robust than Method 2, if Adjusted ∆ OoD > 0 . The results of our statistical analyses are presented in Tables S15 through S26.

D.8 Exploring performance variation of VSSL methods across action classes

To provide a deeper understanding, we conduct a comprehensive analysis examining the impact of distribution shifts on the models' prediction across various action classes. Our investigation focuses on evaluating the performance of the finetuned models across different categories in both the InD and OoD. The findings are presented as follows:

Figure S19 Context shift (10 classes)

Figure S20 Context shift (50 classes)

Figure S21 Viewpoint shift (egocentric)

Figure S22 Viewpoint shift (surveillance camera+low resolution)

Figure S23 Viewpoint + actor shift (top-down + synthetic)

Figure S24 Actor shift (animal)

Figure S25 Source shift (UCF to HMDB)

Figure S26 Source shift (HMDB to UCF)

Figure S28 Zero-shot (UCF)

Figure S27 Zero-shot (HMDB)

Figure S29 Zero-shot (RareAct)

Figure S30 Open-set (UCF/HMDB)

Figure S32 Open-set (Kinetics400/UCF)

Figure S31 Open-set (Kinetics400/HMDB)

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMCLR

In-distibution

Out-of-distibution

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MOCO

In-distibution

Out-of-distibution

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-BYOL

In-distibution

Out-of-distibution

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMSIAM

In-distibution

Out-of-distibution

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-DINO

In-distibution

Out-of-distibution

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MAE

In-distibution

Out-of-distibution

canoeing or kayaking

climbing a rope

driving car

golf driving

opening bottle

playing piano

playing volleyball

shooting goal (soccer)

surfing water

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SUPERVISED

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMCLR

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MOCO

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-BYOL

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMSIAM

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-DINO

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MAE

In-distibution

Out-of-distibution

archery

bowling

brushing hair

brushing teeth

canoeing or kayaking

catching or throwing baseball

catching or throwing frisbee

clean and jerk

cleaning windows

climbing a rope

climbing ladder

deadlifting

dribbling basketball

drinking

driving car

dunking basketball

eating cake

eating ice cream

flying kite

golf driving

hitting baseball

hurdling

juggling balls

juggling soccer ball

opening bottle

playing accordion

playing basketball

playing bass guitar

playing guitar

playing piano

playing saxophone

playing tennis

playing trumpet

playing violin

playing volleyball

punching person (boxing)

reading book

reading newspaper

shooting basketball

shooting goal (soccer)

skiing (not slalom or crosscountry)

skiing slalom

skipping rope

smoking

surfing water

sweeping floor

sword fighting

tying tie

walking the dog

writing

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SUPERVISED

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Accuracy/Precision

v-SIMCLR

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Accuracy/Precision

v-MOCO

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Accuracy/Precision

v-BYOL

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Accuracy/Precision

v-SIMSIAM

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

Accuracy/Precision

v-DINO

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Accuracy/Precision

v-MAE

In-distibution

Out-of-distibution

Closing a book

Closing a box

Closing a closet/cabinet

Closing a door

Closing a laptop

Closing a refrigerator

Closing a window

Drinking from a cup/glass/bot le

Eating a sandwich

Fixing a door

Fixing a doorknob

Fixing a light

Fixing a vacuum

Fixing their hair

Grasping onto a doorknob

Holding a bag

Holding a blanket

Holding a book

Holding a box

Holding a broom

Holding a cup/glass/bot le of something

Holding a dish

Holding a laptop

Holding a mir or

Holding a phone/camera

Holding a picture

Holding a pil ow

Holding a sandwich

Holding a shoe/shoes

Holding a towel/s

Holding a vacuum

Holding some clothes

Holding some food

Holding some medicine

Laughing at a picture

Laughing at television

Lying on a bed

Lying on a sofa/couch

Lying on the floor

Making a sandwich

Opening a bag

Opening a book

Opening a box

Opening a closet/cabinet

Opening a door

Opening a laptop

Opening a refrigerator

Opening a window

Playing with a phone/camera

Pouring something into a cup/glass/bot le

Put ing a bag somewhere

Put ing a blanket somewhere

Put ing a book somewhere

Put ing a box somewhere

Put ing a broom somewhere

Put ing a cup/glass/bot le somewhere

Put ing a dish/es somewhere

Put ing a laptop somewhere

Put ing a phone/camera somewhere

Put ing a picture somewhere

Put ing a pil ow somewhere

Put ing a sandwich somewhere

Put ing a towel/s somewhere

Put ing clothes somewhere

Put ing groceries somewhere

Put ing on shoe/shoes

Put ing shoes somewhere

Put ing some food somewhere

Put ing something on a shelf

Put ing something on a table

Put ing their paper/notebook somewhere

Reaching for and grabbing a picture

Sit ing at a table

Sit ing in a bed

Sit ing in a chair

Sit ing on a table

Sit ing on sofa/couch

Sit ing on the floor

Smiling at a book

Smiling in a mir or

Snuggling with a blanket

Snuggling with a pil ow

Someone is awakening in bed

Someone is awakening somewhere

Someone is cooking something

Someone is dressing

Someone is eating something

Someone is going from standing to sit ing

Someone is holding a paper/notebook

Someone is laughing

Someone is running somewhere

Someone is smiling

Someone is sneezing

Someone is standing up from somewhere

Someone is undressing

Standing on a chair

Taking a bag from somewhere

Taking a blanket from somewhere

Taking a book from somewhere

Taking a box from somewhere

Taking a broom from somewhere

Taking a cup/glass/bot le from somewhere

Taking a dish/es from somewhere

Taking a laptop from somewhere

Taking a phone/camera from somewhere

Taking a picture of something

Taking a pil ow from somewhere

Taking a sandwich from somewhere

Taking a towel/s from somewhere

Taking a vacuum from somewhere

Taking food from somewhere

Taking of  some shoes

Taking paper/notebook from somewhere

Taking shoes from somewhere

Taking some clothes from somewhere

Taking something from a box

Taking/consuming some medicine

Talking on a phone/camera

Throwing a bag somewhere

Throwing a blanket somewhere

Throwing a book somewhere

Throwing a box somewhere

Throwing a broom somewhere

Throwing a pil ow somewhere

Throwing a towel/s somewhere

Throwing clothes somewhere

Throwing food somewhere

Throwing shoes somewhere

Throwing something on the floor

Tidying a shelf or something on a shelf

Tidying some clothes

Tidying something on the floor

Tidying up a blanket/s

Tidying up a closet/cabinet

Tidying up a table

Tidying up a towel/s

Tidying up with a broom

Turning of  a light

Turning on a light

Walking through a doorway

Wash a dish/dishes

Washing a cup/glass/bot le

Washing a mir or

Washing a table

Washing a window

Washing some clothes

Washing something with a towel

Washing their hands

Watching a laptop or something on a laptop

Watching something/someone/themselves in a mir or

Watching television

Watching/Looking outside of a window

Watching/Reading/Looking at a book

Watching/looking at a picture

Working at a table

Working on paper/notebook

Working/Playing on a laptop

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Accuracy/Precision

v-SUPERVISED

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Accuracy/Precision

v-SIMCLR

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Accuracy/Precision

v-MOCO

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Accuracy/Precision

v-BYOL

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Accuracy/Precision

v-SIMSIAM

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Accuracy/Precision

v-DINO

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.20

0.40

0.60

0.80

1.00

Accuracy/Precision

v-MAE

In-distibution

Out-of-distibution

Carrying

Closing

Entering

Exiting

Loading

Opening

Pull

Push

Riding

Running

Standing

Talking

Talking phone

Walking

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Accuracy/Precision

v-SUPERVISED

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-SIMCLR

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-MOCO

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-BYOL

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-SIMSIAM

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-DINO

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-MAE

In-distibution

Out-of-distibution

cook

drink

eat

read book

use phone

walk

0.00

0.20

0.40

0.60

0.80

Acc.

v-SUPERVISED

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMCLR

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MOCO

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-BYOL

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMSIAM

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-DINO

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MAE

In-distibution

Out-of-distibution

drinking

eating

opening door

running

sleeping

swimming

watching tv

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SUPERVISED

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMCLR

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MOCO

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-BYOL

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMSIAM

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-DINO

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MAE

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SUPERVISED

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMCLR

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MOCO

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-BYOL

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SIMSIAM

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-DINO

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-MAE

In-distibution

Out-of-distibution

catch

climb

dive

dribble

fencing

golf

handstand

jump

pullup

punch

pushup

ride bike

ride horse

shoot bow

throw

walk

0.00

0.20

0.40

0.60

0.80

1.00

Acc.

v-SUPERVISED

In-distibution

Out-of-distibution

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

v-SIMCLR

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.10

0.20

0.30

0.40

0.50

0.60

v-MOCO

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

v-BYOL

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

v-SIMSIAM

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

v-DINO

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.20

0.40

0.60

0.80

v-MAE

chew

climb stairs

draw sword

fall floor

fencing

handstand

hit

jump

kick

pick

pour

run

shoot gun

sit

smile

stand

sword exercise

talk

turn

walk

wave

0.00

0.10

0.20

0.30

0.40

0.50

0.60

v-SUPERVISED

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-SIMCLR

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-MOCO

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-BYOL

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-SIMSIAM

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-DINO

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-MAE

apply lipstick

balance beam

billiards

blow dry hair

fencing

field hockey penalty

front crawl

hammering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

military parade

mixing

nunchucks

parallel bars

pizza tossing

playing daf

playing dhol

playing sitar

playing tabla

pommel horse

rafting

rowing

still rings

sumo wrestling

table tennis shot

uneven bars

wall pushups

yo yo

0.00

0.20

0.40

0.60

0.80

1.00

v-SUPERVISED

v-SUPERVISED

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.20

0.40

0.60

0.80

1.00

v-SIMCLR

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

v-MOCO

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.20

0.40

0.60

0.80

1.00

v-BYOL

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.20

0.40

0.60

0.80

1.00

v-SIMSIAM

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.20

0.40

0.60

0.80

v-DINO

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.20

0.40

0.60

0.80

1.00

v-MAE

blend corn

blend phone

blend pumpkin

blend shoes

cut book

cut car

cut chair

cut coconut

cut keyboard

cut laptop

cut phone

cut pumpkin

cut rock

cut shoes

cut towel

deseed pepper

dril  book

dril  bottle

dril  eggs

dril  laptop

dril  phone

dril  pumpkin

dril  rock

dril  tomato

dril  watermelon

drink chocolate

drink egg

fry phone

hammer banana

hammer bottle

hammer car

hammer coconut

hammer egg

hammer flower

hammer fridge

hammer keyboard

hammer laptop

hammer microwave

hammer phone

hammer pumpkin

hammer rock

hammer shoes

hammer tomato

hammer watermelon

measure carrots

measure chair

measure egg

measure fridge

measure hair

measure laptop

measure microwave

measure oven

measure phone

measure pumpkin

measure shoes

measure watermelon

microwave book

microwave bottle

microwave laptop

microwave phone

microwave shoes

microwave watermelon

move towel

open blender

open microwave

peel coconut

peel corn

peel watermelon

rol  banana

rol  carrot

rol  potato

rol  shirt

shake chair

shake clothe

shake flower

shake hair

shake table

shake tree

spray book

spray chair

spray door

spray eggs

spray laptop

spray microwave

spray phone

spray shoes

spray table

throw flower

throw orange

throw shirt

throw spoon

unplug fridge

unplug oven

unplug phone

wash apple

wash bicycle

wash blender

wash chair

wash cucumber

wash door

wash fridge

wash laptop

wash microwave

wash oven

wash pepper

wash rock

wash towel

wash watermelon

wash window

weigh book

weigh bottle

weigh egg

weigh phone

weigh pumpkin

weigh shoes

weigh watermelon

0.00

0.20

0.40

0.60

0.80

1.00

v

-SimCLR

v

-MoCo

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

v

-BYOL

v

-SimSiam

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

v

-DINO

v

-MAE

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

apply eye makeup

apply lipstick

archery

baby crawling

balance beam

band marching

basketbal

basketbal  dunk

bench press

biking

blow dry hair

blowing candles

body weight squats

bowling

boxing punching bag

boxing speed bag

breast stroke

brushing teeth

clean and jerk

clif  diving

cricket bowling

cricket shot

cut ing in kitchen

diving

drumming

fencing

field hockey penalty

floor gymnastics

frisbee catch

front crawl

golf swing

haircut

hammer throw

hammering

handstand pushups

handstand walking

head massage

high jump

horse race

horse riding

hula hoop

ice dancing

javelin throw

juggling bal s

jumping jack

kayaking

knit ing

long jump

lunges

military parade

mixing

mopping floor

nunchucks

pizza tossing

playing cel o

playing daf

playing dhol

playing flute

playing guitar

playing piano

playing sitar

playing tabla

playing violin

pole vault

pommel horse

pul  ups

punch

push ups

rafting

rock climbing indoor

rope climbing

rowing

salsa spin

shaving beard

shotput

skate boarding

ski ng

skijet

sky diving

soccer juggling

soccer penalty

stil  rings

sumo wrestling

surfing

swing

table tennis shot

tennis swing

throw discus

trampoline jumping

typing

uneven bars

vol eybal  spiking

walking with dog

wal  pushups

writing on board

yo yo

brush hair

cartwheel

chew

clap

climb stairs

drink

eat

fal  floor

hit

hug

kick

kiss

laugh

pick

pour

push

run

shake hands

shoot bal

shoot bow

shoot gun

sit

situp

smile

smoke

somersault

stand

swing basebal

sword exercise

talk

turn

wave

v

-SimCLR

v

-MoCo

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush painting

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counting money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunting

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanting

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planting tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nting weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray painting

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

vault

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

chew

climb stairs

draw sword

r

fencing

handstand

hit

jump

kick

p

k

pour

r

n

sho

t gun

sit

smile

stand

rcise

talk

urn

walk

w ve

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blasting

a d

ow

l

s

bl

ing leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

bow

g

braid ng hair

breading or bre

dcrumbing

breakdanc

ush p

i

ting

br

ing hair

te

th

build ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erlead ng

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

exti

guish ng fire

facepla

t ng

e

ding birds

f

e

f

h

fe

ding

oats

fil ng ey

nger snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

e col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ice climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc er b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas ering

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

playing cymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombone

playing trump

t

playing vol eybal

xy

phone

pole vault

pres

nting weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

ta

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

trim

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk ng the dog

shi

dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

chew

climb stairs

draw

wo d

r

encing

fli

flac

handst nd

hit

jump

kick

p

our

r

n

sho

t gu

sit

sm le

tand

rcise

talk

urn

walk

ve

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush painting

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counting money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunting

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanting

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planting tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nting weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray painting

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

vault

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

chew

climb stairs

draw sword

r

fencing

handstand

hit

jump

kick

p

k

pour

r

n

sho

t gun

sit

smile

stand

rcise

talk

urn

walk

w ve

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blasting

a d

ow

l

s

bl

ing leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

bow

g

braid ng hair

breading or bre

dcrumbing

breakdanc

ush p

i

ting

br

ing hair

te

th

build ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erlead ng

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

exti

guish ng fire

facepla

t ng

e ding birds

f

e

f

h

fe

ding

oats

fil ng ey

nger snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

e col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ice climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc er b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas ering

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

playing cymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t

n is

playing trombone

playing trump

t

playing vol eybal

xy

phone

pole vault

pres

nting weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p

ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

ta

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

trim

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk ng the dog

shi

dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

chew

climb stairs

draw

wo d

r

encing

fli

flac

handst nd

hit

jump

kick

p

our

r

n

sho

t gu

sit

sm le

tand

rcise

talk

urn

walk

ve

v

-BYOL

v

-SimSiam

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush painting

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counting money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunting

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanting

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planting tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nting weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray painting

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

vault

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

chew

climb stairs

draw sword

r

fencing

handstand

hit

jump

kick

p

k

pour

r

n

sho

t gun

sit

smile

stand

rcise

talk

urn

walk

w ve

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blasting

a d

ow

l

s

bl

ing leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

bow

g

braid ng hair

breading or bre

dcrumbing

breakdanc

ush p

i

ting

br

ing hair

te

th

build ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erlead ng

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

exti

guish ng fire

facepla

t ng

e

ding birds

f

e

f

h

fe

ding

oats

fil ng ey

nger snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

e col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ice climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc er b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas ering

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

playing cymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombone

playing trump

t

playing vol eybal

xy

phone

pole vault

pres

nting weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

ta

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

trim

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk ng the dog

shi

dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

chew

climb stairs

draw

wo d

r

encing

fli

flac

handst nd

hit

jump

kick

p

our

r

n

sho

t gu

sit

sm le

tand

rcise

talk

urn

walk

ve

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush painting

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counting money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunting

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanting

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planting tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nting weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray painting

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

vault

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

chew

climb stairs

draw sword

r

fencing

handstand

hit

jump

kick

p

k

pour

r

n

sho

t gun

sit

smile

stand

rcise

talk

urn

walk

w ve

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blasting

a d

ow

l

s

bl

ing leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

bow

g

braid ng hair

breading or bre

dcrumbing

breakdanc

ush p

i

ting

br

ing hair

te

th

build ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erlead ng

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

exti

guish ng fire

facepla

t ng

e ding birds

f

e

f

h

fe

ding

oats

fil ng ey

nger snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

e col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ice climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc er b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas ering

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

playing cymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t

n is

playing trombone

playing trump

t

playing vol eybal

xy

phone

pole vault

pres

nting weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p

ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

ta

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

trim

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk ng the dog

shi

dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

chew

climb stairs

draw

wo d

r

encing

fli

flac

handst nd

hit

jump

kick

p

our

r

n

sho

t gu

sit

sm le

tand

rcise

talk

urn

walk

ve

v

-DINO

v

-MAE

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breakdancing

brush painting

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

king on campfire

co

king sausages

counting money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

am style

dancing mac

rena

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunting

ex rcis ng arm

rcise bal

extinguishing fire

faceplanting

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

tbal  (in game)

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planting tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

nting weather forecast

pul  ups

pumping fist

pumping

as

punching bag

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

singing

s tup

skateboarding

ski jumping

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray painting

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

ing backstroke

ing breast

roke

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob

tos

ing coin

tos

ing sal d

train

g dog

trapezing

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using segway

vault

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

chew

climb stairs

draw sword

r

fencing

handstand

hit

jump

kick

p

k

pour

r

n

sho

t gun

sit

smile

stand

rcise

talk

urn

walk

w ve

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blasting

a d

ow

l

s

bl

ing leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

bow

g

braid ng hair

breading or bre

dcrumbing

breakdanc

ush p

i

ting

br

ing hair

te

th

build ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erlead ng

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

exti

guish ng fire

facepla

t ng

e

ding birds

f

e

f

h

fe

ding

oats

fil ng ey

nger snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

e col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ice climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc er b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas ering

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

playing cymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombone

playing trump

t

playing vol eybal

xy

phone

pole vault

pres

nting weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

ta

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

trim

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk ng the dog

shi

dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

chew

climb stairs

draw

wo d

r

encing

fli

flac

handst nd

hit

jump

kick

p

our

r

n

sho

t gu

sit

sm le

tand

rcise

talk

urn

walk

ve

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breakdancing

brush painting

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

king on campfire

co

king sausages

counting money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

am style

dancing mac

rena

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunting

ex rcis ng arm

rcise bal

extinguishing fire

faceplanting

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

tbal  (in game)

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planting tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

nting weather forecast

pul  ups

pumping fist

pumping

as

punching bag

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

singing

s tup

skateboarding

ski jumping

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray painting

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

ing backstroke

ing breast

roke

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob

tos

ing coin

tos

ing sal d

train

g dog

trapezing

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using segway

vault

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

chew

climb stairs

draw sword

r

fencing

handstand

hit

jump

kick

p

k

pour

r

n

sho

t gun

sit

smile

stand

rcise

talk

urn

walk

w ve

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blasting

a d

ow

l

s

bl

ing leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

bow

g

braid ng hair

breading or bre

dcrumbing

breakdanc

ush p

i

ting

br

ing hair

te

th

build ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erlead ng

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

exti

guish ng fire

facepla

t ng

e ding birds

f

e

f

h

fe

ding

oats

fil ng ey

nger snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

e col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ice climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc er b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas ering

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

playing cymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t

n is

playing trombone

playing trump

t

playing vol eybal

xy

phone

pole vault

pres

nting weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p

ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

ta

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

trim

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk ng the dog

shi

dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

chew

climb stairs

draw

wo d

r

encing

fli

flac

handst nd

hit

jump

kick

p

our

r

n

sho

t gu

sit

sm le

tand

rcise

talk

urn

walk

ve

pas ing American fo breading or breadcrumbing

catching or throwing basebal catching or throwing frisbe

catching or throwing softbal decorating the christmas tre

ex rcis ng with an ex giv ng or  ec

mas aging person's head pas ing American fo

playing squash or  acquetbal pres

punching person (boxing)

rid ng or walking with sign language interp eting

ski ng (not sla om or c os country)

swim swim

trim using remote control er (not gaming)

breading or breadcrumbing catching or throwing basebal

catching or throwing frisbe catching or throwing softbal

decorating the christmas tre ex rcis ng with an ex

giv ng or  ec mas aging person's head

pas ing American fo pas ing American fo

playing squash or  acquetbal pres

punching person (boxing)

rid ng or walking with sign language interp eting

ski ng (not sla om or c os country)

swim swim

trim using remote control er (not gaming)

co dancing

run sho

swim co

dancing run

sho swim

pas ing American fo pas ing American fo

pas ing American fo

v

-SimCLR

v

-MoCo

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush paint

g

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counti

g money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanti g

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planti

g tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nti

g weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray paint

g

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

v

ult

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

ap

bal nce beam

bil

ards

blow dry hair

fencing

field hockey penalty

front crawl

ham

ering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

mix ng

nu

chucks

par

l e  bars

piz

a tos

ing

playing daf

playing dhol

playing sitar

playing tabla

pom

el horse

rafting

rowing

stil  rings

sumo wrestling

table ten

is  hot

unev n bars

wal  pushups

yo yo

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

au tioni g

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blastin

a d

ow

g

l

s

bl

in

leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

b w

g

braid ng hair

breading or bre

dcrumbing

akdanc

brush p

i

t ng

ing hair

brush

te

th

uild ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erl

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise b

l

exti

guish ng fire

facepla

t ng

e

ding birds

f

e

f

h

fe

ding

oats

fil ng ey

er snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

golf put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

eadbanging

h

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o ping

hurdl

hurling (sport)

ce climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc

r b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas

ring

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

ymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombo

e

ump t

playing

k

le

playing vol eybal

xy

phone

pole vault

pres

nti

g weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowb a d ng

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

t im

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk

the dog

shing dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

ap

b lance beam

bil

rds

blow dry hair

fencing

field hockey p

alty

front cr

wl

ham

e ing

handstand pushups

walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bar

iz

a tos

ing

pl

ying daf

a

hol

pl

a

tabl

pom

el horse

rafting

ow

stil  rings

sumo wrestl

table ten

i

hot

un ven bars

wal  pushup

yo yo

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush paint

g

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counti

g money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanti g

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planti

g tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nti

g weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray paint

g

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

v

ult

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

ap

bal nce beam

bil

ards

blow dry hair

fencing

field hockey penalty

front crawl

ham

ering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

mix ng

nu

chucks

par

l e  bars

piz

a tos

ing

playing daf

playing dhol

playing sitar

playing tabla

pom

el horse

rafting

rowing

stil  rings

sumo wrestling

table ten

is  hot

unev n bars

wal  pushups

yo yo

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

au tioni g

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blastin

a d

ow

g

l

s

bl

in

leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

b w

g

braid ng hair

breading or bre

dcrumbing

akdanc

brush p

i

t ng

ing hair

brush

te

th

uild ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erl

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise b

l

exti

guish ng fire

facepla

t ng

e

ding birds

f e

f

h

fe

ding

oats

fil ng ey

er snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

golf put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

eadbanging

h

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ce climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc

r b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas

ring

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

ymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombo

e

ump t

playing

k

le

playing vol eybal

xy

phone

pole vault

pres

nti

g weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p

ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowb a d ng

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

t im

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk

the dog

shing dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

ap

b lance beam

bil

rds

blow dry hair

fencing

field hockey p

alty

front cr

wl

ham

e ing

handstand pushups

walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bar

iz

a tos

ing

pl

ying daf

a

hol

pl

a

tabl

pom

el horse

rafting

ow

stil  rings

sumo wrestl

table ten

i

hot

un ven bars

wal  pushup

yo yo

v

-BYOL

v

-SimSiam

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush paint

g

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counti

g money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanti g

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planti

g tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nti

g weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray paint

g

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

v

ult

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

ap

bal nce beam

bil

ards

blow dry hair

fencing

field hockey penalty

front crawl

ham

ering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bars

piz

a tos

ing

playing daf

playing dhol

playing sitar

playing tabla

pom

el horse

rafting

rowing

stil  rings

sumo wrestling

table ten

is  hot

unev n bars

wal  pushups

yo yo

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

au tioni g

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blastin

a d

ow

g

l

s

bl

in

leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

b w

g

braid ng hair

breading or bre

dcrumbing

akdanc

brush p

i

t ng

ing hair

brush

te

th

uild ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erl

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise b

l

exti

guish ng fire

facepla

t ng

e

ding birds

f

e

f

h

fe

ding

oats

fil ng ey

er snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

golf put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

eadbanging

h

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o ping

hurdl

hurling (sport)

ce climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc

r b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas

ring

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

ymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombo

e

ump t

playing

k

le

playing vol eybal

xy

phone

pole vault

pres

nti

g weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowb a d ng

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

t im

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk

the dog

shing dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

ap

b lance beam

bil

rds

blow dry hair

fencing

field hockey p

alty

front cr

wl

ham

e ing

handstand pushups

walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bar

iz

a tos

ing

pl

ying daf

a

hol

pl

a

tabl

pom

el horse

rafting

ow

stil  rings

sumo wrestl

table ten

i

hot

un ven bars

wal  pushup

yo yo

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breading or breadcrumbing

breakdancing

brush paint

g

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

catching or throwing basebal

catching or throwing frisbe

catching or throwing softbal

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

co

king on campfire

co

king sausages

counti

g money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

dancing

am style

dancing mac

rena

decorating the christmas tre

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise bal

extinguishing fire

faceplanti g

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

giv ng or  ec

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

mas aging person's head

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planti

g tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing squash or  acquetbal

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

pres

nti

g weather forecast

pul  ups

pumping fist

pumping

as

punching bag

punching person (boxing)

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

rid ng or walking with

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

run

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

sign language interp eting

singing

s tup

skateboarding

ski jumping

ski ng (not sla om or c os country)

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray paint

g

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

swim

ing backstroke

swim

ing breast

roke

swim

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob g

tos

ing coin

tos

ing sal d

train

g dog

trapezing

trim

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using remote control er (not gaming)

using segway

v

ult

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

ap

bal nce beam

bil

ards

blow dry hair

fencing

field hockey penalty

front crawl

ham

ering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bars

piz

a tos

ing

playing daf

playing dhol

playing sitar

playing tabla

pom

el horse

rafting

rowing

stil  rings

sumo wrestling

table ten

is  hot

unev n bars

wal  pushups

yo yo

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

au tioni g

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blastin

a d

ow

g

l

s

bl

in

leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

b w

g

braid ng hair

breading or bre

dcrumbing

akdanc

brush p

i

t ng

ing hair

brush

te

th

uild ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erl

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise b

l

exti

guish ng fire

facepla

t ng

e

ding birds

f e

f

h

fe

ding

oats

fil ng ey

er snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

golf put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

eadbanging

h

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ce climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc

r b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas

ring

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

ymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombo

e

ump t

playing

k

le

playing vol eybal

xy

phone

pole vault

pres

nti

g weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p

ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowb a d ng

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

t im

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk

the dog

shing dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

ap

b lance beam

bil

rds

blow dry hair

fencing

field hockey p

alty

front cr

wl

ham

e ing

handstand pushups

walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bar

iz

a tos

ing

pl

ying daf

a

hol

pl

a

tabl

pom

el horse

rafting

ow

stil  rings

sumo wrestl

table ten

i

hot

un ven bars

wal  pushup

yo yo

v

-DINO

v

-MAE

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breakdancing

brush paint

g

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

king on campfire

co

king sausages

counti

g money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

am style

dancing mac

rena

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunti g

ex rcis ng arm

rcise bal

extinguishing fire

faceplanti g

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

tbal  (in game)

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planti

g tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

g weather forecast

pul  ups

pumping fist

pumping

as

punching bag

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

singing

s tup

skateboarding

ski jumping

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray paint

g

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

ing backstroke

ing breast

roke

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob

tos

ing coin

tos

ing sal d

train

g dog

trapezing

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using segway

v

ult

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

ap

bal nce beam

bil

ards

blow dry hair

fencing

field hockey penalty

front crawl

ham

ering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

mix ng

nu

chucks

par

l e  bars

piz

a tos

ing

playing daf

playing dhol

playing sitar

playing tabla

pom

el horse

rafting

rowing

stil  rings

sumo wrestling

table ten

is  hot

unev n bars

wal  pushups

yo yo

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

au tioni g

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blastin

a d

ow

g

l

s

bl

in

leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

b w

g

braid ng hair

breading or bre

dcrumbing

akdanc

brush p

i

t ng

ing hair

brush

te

th

uild ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erl

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t

i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise b

l

exti

guish ng fire

facepla

t ng

e

ding birds

f

e

f

h

fe

ding

oats

fil ng ey

er snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

golf put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

eadbanging

h

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o ping

hurdl

hurling (sport)

ce climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc

r b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas

ring

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

ymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombo

e

ump t

playing

k

le

playing vol eybal

xy

phone

pole vault

pres

nti

g weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowb a d ng

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

t im

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk

the dog

shing dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

ap

b lance beam

bil

rds

blow dry hair

fencing

field hockey p

alty

front cr

wl

ham

e ing

handstand pushups

walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bar

iz

a tos

ing

pl

ying daf

a

hol

pl

a

tabl

pom

el horse

rafting

ow

stil  rings

sumo wrestl

table ten

i

hot

un ven bars

wal  pushup

yo yo

air drum

ing

answering questions

ap

lauding

ap

lying cream

archery

arm wrestling

ar anging flowers

as embling computer

ba

y waking up

baking co

kies

bal o

n blowing

bandaging

barbequing

bartending

beatboxing

be  ke

ping

bel y dancing

bench pres ing

bending back

bending metal

bik ng through snow

blasting sand

blowing

las

blowing leaves

blowing nose

blowing out candles

bo

sled

ing

bo kbinding

bouncing on trampoline

bowling

braid ng hair

breakdancing

brush paint

g

brushing hair

brushing te

th

build ng cabinet

build ng shed

bunge  jumping

busking

canoeing or kay

king

capoeira

car ying ba

y

cartwhe

ling

carving pumpkin

catching fish

cel

brating

changing oil

changing whe

l

checking tires

che

rleading

chop

ing wo d

clap

ing

clay pot ery making

clean and jerk

cleani

g flo

r

cleani

g

ut ers

cleani

g po

l

cleani

g shoes

cleani

g toilet

cleani

g windows

climbing a rope

climbing lad

er

climbing tre

contact jug

ling

co

king chicken

co

king eg

king on campfire

co

king sausages

counti

g money

country line dancing

cracking neck

crawling ba

y

cros

ing river

crying

curling hair

cut ing nails

cut ing pineap

le

cut ing watermelon

dancing bal et

dancing charleston

am style

dancing mac

rena

dig

ing

din

g

disc golfing

div ng clif

do

gebal

doing aerobics

doing laundry

doing nails

drawing

ling basketbal

drinking

drinking be

r

drinking shots

driv ng tractor

drop kicking

drum

ing fingers

dunking basketbal

dying hair

eating burger

eating cake

eating car ots

eating chips

eating doughnuts

eating hotdog

eating ice cream

eating spaghet i

eating watermelon

eg

hunti g

ex rcis ng arm

rcise bal

extinguishing fire

faceplanti g

fe

ding birds

fe

ding fish

fe

ding

oats

fil ng ey

brows

finger snap

ing

fix ng hair

flip

ing pancake

flying kite

folding clothes

folding napkins

folding pa

er

front raise

frying veg

tables

garbage col ecting

gargling

get ing a haircut

get ing a ta to

iv ng award

golf chip

ing

golf put ing

grinding meat

gro

ming dog

gro

ming horse

gymnastics tumbling

ham

er throw

headbanging

headbut ing

high jump

high kick

hit

ng basebal

hockey stop

holding snake

hopscotch

hoverboarding

hug

ing

ping

hurdling

hurling (sport)

ice climbing

ice fishing

ice skating

javelin throw

jog

ing

jug

ling bal s

jug

ling fire

jug

ling soc

er bal

jumping into po

l

jumpstyle dancing

kicking field goal

kicking soc

er bal

kis

ing

kitesurfing

knit

ng

krumping

laughing

laying bricks

long jump

lunge

making a cake

making a sandwich

making bed

making jewelry

making piz a

making snowman

making su hi

making tea

marching

mas aging back

t

mas aging legs

milk ng cow

mop ing flo

r

mot rcy ling

moving furniture

mowing lawn

news anchoring

openi

g bot le

openi

g pres

nt

par

glid ng

par

parkour

tbal  (in game)

tbal  (not in game)

pe ling ap

les

pe ling pota oes

pet ing animal (not cat)

pet ing cat

picking fruit

planti

g tre

s

plastering

playing ac

ordion

playing badminton

playing bagpi

es

playing basketbal

playing cards

playing cel o

playing ches

playing clarinet

playing control er

playing cri

ket

playing cymbals

playing di

gerido

playing drums

playing flute

playing

uitar

playing harmonica

playing harp

playing ice hockey

playing keyboard

playing kickbal

playing mon

poly

playing organ

playing paintbal

playing piano

playing poker

playing recorder

playing saxophone

playing ten

is

playing trombone

playing trumpet

playing uk

le

playing viol n

playing vol eybal

playing xylophone

pole vault

g weather forecast

pul  ups

pumping fist

pumping

as

punching bag

push up

pushing car

pushing cart

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

rid ng a bike

rid ng camel

phant

rid ng mechanical bul

rid ng mountain bike

rid ng mule

orse

rid ng sco

ter

rid ng unicy

le

rip

ing pa

er

t dancing

rock climbing

rock scis

ors pa

er

rol er skating

ing on treadmil

salsa dancing

sanding flo

r

scrambling eg

s

set ing table

shaking hands

shaking head

sharpeni

g knives

sharpeni

g pencil

shaving head

shaving legs

shearing she p

shin

g shoes

sho

ting

oal (soc

er)

shot put

shoveling snow

shred

ing pa

er

shuf ling cards

side kick

singing

s tup

skateboarding

ski jumping

ski ng cros country

ski ng sla om

skip

ing rope

skydiv ng

slacklin

g

slap

ing

sled

og racing

smoking

smoking ho

snatch weight lift ng

sne

zing

snif ng

snorkeling

snowboarding

snowkit ng

snowmobil ng

somersaulting

spin

ing poi

spray paint

g

spraying

springboard

iv ng

squat

sticking tongue out

stomping

rapes

stretching arm

stretching leg

strum

ing

uitar

surfing crowd

surfing water

swe ping flo

r

ing backstroke

ing breast

roke

ing but erfly stroke

swing dancing

swinging legs

swinging on something

sword fighting

taking a shower

tango dancing

tap dancing

tap

ing

uitar

tap

ing pen

tasting be

r

tasting fo d

testify ng

texting

throwing axe

throwing bal

throwing discus

tickling

tob

tos

ing coin

tos

ing sal d

train

g dog

trapezing

ing or shaving beard

trim

ing tre

s

triple jump

tying bow tie

tying tie

unboxing

unloading truck

using computer

using segway

v

ult

wait ng in line

walking the dog

washing dishes

washing fe

t

washing hair

washing hands

water ski ng

water slid ng

watering plants

waxing back

waxing chest

waxing ey

brows

waxing legs

weaving basket

welding

whistling

windsurfing

wrap

ing pres

nt

wrestling

yawni g

yoga

zumba

ap

bal nce beam

bil

ards

blow dry hair

fencing

field hockey penalty

front crawl

ham

ering

handstand pushups

handstand walking

horse race

ice dancing

jumping jack

mix ng

nu

chucks

par

l e  bars

piz

a tos

ing

playing daf

playing dhol

playing sitar

playing tabla

pom

el horse

rafting

rowing

stil  rings

sumo wrestling

table ten

is  hot

unev n bars

wal  pushups

yo yo

air drum

answer ng q

estions

ap

laud

g

ap

lying cream

a

h ry

arm wrestling

ar

nging flowers

as embli

comput

au tioni g

ba

y wak

g up

king co

kies

bal o

bl

bandagi

rbequ ng

ba t

ndi

e

b

ke

pi

bench pres i

bending back

metal

bik ng through snow

blastin

a d

ow

g

l

s

bl

in

leave

blowing nos

blowing

ut ca

dle

bo

sle

ing

kbind

bouncing on trampoline

b w

g

braid ng hair

breading or bre

dcrumbing

akdanc

brush p

i

t ng

ing hair

brush

te

th

uild ng cabine

build ng sh d

bunge  jumping

b sk

canoeing or kay

ing

c

poe ra

car ying ba

y

twhe

ling

carving pumpk

catching fish

catching or throwing basebal

fri

e

catching or throwing softbal

cel

bra ing

hanging oil

changi

whe

check ng tir

s

erl

chop

ing wo d

clap

ing

clay pot ery m

k

cl

an and jerk

e

i

g flo

cleani

g

ut ers

cleani

g po

l

sh

es

cleani

g toil

t

cleani

g wind ws

cl mbing a rope

i

l d

r

climbing tre

contact ju

ling

king chicke

co

king  g

co

king

n campfire

co

king s

usag

s

u

ting money

country l

cracking

eck

wl

ba

y

cros

ing river

c y ng

curling hair

t

n

ls

cut ing pineap

e

cut ing wat

rmelon

dancing ba

et

dancing

harleston

danci

g

a

n m

dancing mac

rena

de dlift

g

decorating the christmas  re

dig

ing

n

disc golfing

vin

cl f

do

geba l

doing aero

ics

l

undry

doing

ails

draw ng

ling basketbal

drinking

drinking be

r

shots

to

drop kicking

drum

ing f ngers

unking basketbal

dying h

ir

eating burge

eating cak

eating car ots

eating chip

nuts

eating hotdog

eating  ce cream

spagh

t i

eating watermelon

eg

hunti g

ex rcis ng arm

ex rcis ng with an ex

rcise b

l

exti

guish ng fire

facepla

t ng

e

ding birds

f e

f

h

fe

ding

oats

fil ng ey

er snap

ing

fix

g hair

flip

ing panc

ke

flyi

g

it

folding clothes

foldi

g

aper

front r

is

frying veg

ables

ga ba

col ecting

gargl

get ing a h

ircut

t

ta to

giv ng or  ec

iv ng

ward

golf chip

ing

golf put ing

grinding meat

o m

dog

gro

ming horse

gymnastics tumbling

ham

er throw

eadbanging

h

ut

high j mp

kick

hit

ng basebal

hockey  top

holding snake

hop cotch

hoverb

arding

hug

hula

o

ping

hurdl

hurling (sport)

ce climbing

i

e f sh

c

skating

iron

javel n throw

og

jug

g

g fire

jug

ling soc

er bal

jumping into po

style dancing

kicking field goal

soc

r b

kis

ing

kitesurf

knit

ng

krumpi

la

laying bricks

lo

jump

l

nge

making a cak

making

sandwich

g bed

making jew

lry

making piz a

making s owm n

making su hi

making tea

marching

mas aging back

s

fe

t

ma

aging l

gs

mas aging perso

's head

milk ng cow

mop i

fl

r

torcy

ing

moving fu n ture

mowing lawn

news a

chori g

openi

g b

t le

openi

g pres

nt

par

glid

g

as

p rkour

pas ing American fo

tbal  (in game)

pas ing American fo

tbal  (not

pe ling ap

les

pe ling pot

o

pet ing an mal (n

cat)

pet i g

ick n

fruit

planti

g t e

s

plas

ring

playing

c

o d o

badmint n

playing bagp

es

sketbal

playing ba

gui

r

playin

cards

g

el o

playin

ch

s

playing clarinet

playing cont ol

r

playing cri

ket

ymbals

playing di

gerido

play n

d ums

playing fl

te

playing harmon c

pl

playing ice hockey

k yb ard

l

playing mon

po y

playing organ

playing paintb

l

playing piano

oker

playing rec

rd

playing saxophone

playing squ

sh or

cquetbal

playing t n

is

playing trombo

e

ump t

playing

k

le

playing vol eybal

xy

phone

pole vault

pres

nti

g weather f

r

c

s

pul  up

p

a

punching b g

punching perso

(boxin

)

push up

pushing car

t

pushing whe

lchair

reading bo

k

reading newspa

er

recording music

i

a b ke

r ding c m l

phant

rid ng mechanical bul

i

ou

ta n

ike

rid ng mul

rid ng or walking with

orse

r ding sc

t

r

i

uni

ycle

r p

ing pa

r

ob t dancing

r

ck climb

rock scis

ors pa

er

rol e

k

ting

run

ing

n treadm l

s

salsa danc

n

ing flo

r

scrambl

eg

s

a div ng

et ing table

shak

h

nds

ing

ea

sharpen

kniv

s

ing pencil

shav

h ad

ing legs

shearing sh

p

sh

oes

sho

sho

ting

o

l ( oc er)

sh

t put

shoveling snow

red

pa

er

shuf ling c

rds

s de kick

sign language interp

t ng

s tup

skateboarding

ski jump

ski ng (not sla om or c o sco

ntry)

ski ng

r

u

ski ng sla om

p

ing r pe

skydiv ng

slap

ng

sled

og r

smok ng

smoking h

ah

snatch we

t lift ng

sne

zi

if ng

snorkeli

snowb a d ng

snowkit

snowmob l ng

omersau ti

spin

ing poi

ting

s

r

y

springboard

iv ng

squat

sticking tongue o

stompi

rapes

stretching  rm

leg

strum

ing

uitar

surf

crowd

ing water

swe p

flo

swim

ing backstr

ke

swim

ing bre

st

o

swim

ing but

rfly str

ke

swing dancing

ing le

s

swinging on something

taking a shower

o dancing

tap

tap

ing

uitar

tap

ing pen

st

b

r

ta

ing fo d

testi y ng

exti

throwing axe

b l

throwing discus

t ckling

tob g

an

s

ing coin

to

sal d

rain

g dog

trapezin

trim

ing or shaving b

ard

t im

tre

s

triple jump

tying bow tie

)

yi

g tie

unbox ng

unloadi

g truck

si

g comp

ter

using remote control er (not ga

ing)

usi

segway

v

ult

wait ng in line

walk

the dog

shing dishes

washing f

t

hair

washing hands

water ski

g

l d n

watering p a

ts

wax

b

ck

ing chest

waxing ey

brow

waxing legs

weaving bask

t

welding

hist

wind urfing

wrap

g pres

t

w

tling

wr

yo

a

zumb

ap

b lance beam

bil

rds

blow dry hair

fencing

field hockey p

alty

front cr

wl

ham

e ing

handstand pushups

walking

horse race

ice dancing

jumping jack

mix ng

nu chucks

par

l e  bar

iz

a tos

ing

pl

ying daf

a

hol

pl

a

tabl

pom

el horse

rafting

ow

stil  rings

sumo wrestl

table ten

i

hot

un ven bars

wal  pushup

yo yo

pas ing American fo breading or breadcrumbing

catching or throwing basebal catching or throwing frisbe

catching or throwing softbal decorating the christmas tre

ex rcis ng with an ex giv ng or  ec

mas aging person's head pas ing American fo

playing squash or  acquetbal nti

pres punching person (boxing)

rid ng or walking with sign language interp eting

ski ng (not sla om or c os country)

swim swim

trim using remote control er (not gaming)

breading or breadcrumbing catching or throwing basebal

catching or throwing frisbe catching or throwing softbal

decorating the christmas tre ex rcis ng with an ex

giv ng or  ec mas aging person's head

pas ing American fo pas ing American fo

playing squash or  acquetbal nti

pres punching person (boxing)

rid ng or walking with sign language interp eting

ski ng (not sla om or c os country)

swim swim

trim using remote control er (not gaming)

co dancing

run sho

swim co

dancing run

sho swim

Peer reviews:
comments: SUMMARY:
The authors studied the behavior of six popular self-supervised methods in response to various forms of natural distribution shift. And the study uncovers a series of interesting findings and behaviors of video self-supervised learning (VSSL) methods. The experiments and results are beneficial for the video representation learning community.

STRENGTHS:
1. Extensive experiments are conducted. And sufficient experimental data and analysis are provided. It seems to the reviewer that all the conclusions are grounded.

WEAKNESSES:
1. It seems that the effects of data size and model size are not discussed.

2. Many experiments are conducted and some conclusions are obtained. But no improved VSSL are provided, which might be more interesting.

QUESTIONS:
1. Any suggestions for the future research to design better DSSL method?

2. Providing a table of summarisation of the 6 methods might be helpful for the readers. There are many experiments and data.

3. The methods are pre-trained on K400 and K700 datasets, which might not be large enough. If the models are pre-trained on a much larger dataset, will the conclusions in this paper be changed?

4. Does the model sizes effect the conclusions in this paper ?

LIMITATIONS:
Limitations have been discussed in the text. And no obvious negative societal impacts.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper studies the generalizability of many self-supervised training approaches in the video domain. The paper uses 17 tasks to completely test different aspects of the models, including view-point change, temporal modeling, and open set generalizability.\n\nSTRENGTHS:\n1. Most of the cutting-edge video pre-training approaches are covered.\n2. Generalizability is an important problem for current large foundation models.\n\nWEAKNESSES:\n1. It's good to have a hyper-parameter table in the appendix. However, I am not sure if the authors have swept hyper-parameters for each method, and make sure each pre-training is complete. I understand the training for each approach is expensive, but the value will be reduced if the pre-training recipe hasn't been fully explored. \n2. Given the paper studies the pros and cons of each approach, I would expect there will be a proposed approach that performs better in most cases or a guideline that can help readers to develop such a method.\n3. Now the conclusion of each question scatters in different sections. This is fine but I think having a summary table to compare all approaches and show the findings at the beginning or at the end could help readers to understand the pros and cons of each approach better.\n\nQUESTIONS:\n1. How is the v-supervised trained? Does it use kinetics' labeled data for pre-training?\n2. In lines 194 - 198 and Table 1, I didn't see the OoD performance of v-supervised and v-MAE as better than the others if they learn better time-variant information. Most of the time Table 1 shows mixed results in linear probing and fine-tuning (e.g. v-MAE performs better in OoD with FT but worse in Lin.).\n3. Maybe this paper can consider citing [1, 2] in lines 219 - 222.\n4. In Table 5, why the trend of fine-tuning and linear probing is very different?\n\n[1] Lei, Jie, Tamara L. Berg and Mohit Bansal. “Revealing Single Frame Bias for Video-and-Language Learning.”\n[2] Buch, S., Cristobal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei and Juan Carlos Niebles. “Revisiting the “Video” in Video-Language Understanding.”\n\n---\n**Post-rebuttal**\nThank you for the authors' response. I have read it and it addressed my questions.\n\nLIMITATIONS:\nThe limitation is adequate.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a set of benchmarks to assess different robustness properties of video representation learning models, including contrastive, non-contrastive, and generative models. The paper trains multiple of these models using a common training protocol and reports multiple empirical findings on different forms of distribution shift.\n\nSTRENGTHS:\nThe paper outlines a very clear training and evaluation protocol and nicely outlines the results. I like the \"highlights\" section at the end of each addressed question section (up to comments below). The considered datasets and models are extensive. Benchmark and models could become a very valuable resource for further robustness benchmarking on video datasets.\n\nWEAKNESSES:\n- One major weakness of the paper seems to be the missing control for the baseline performance of the proposed methods. For instance, in Table 2, several claims are made with respect to the performance of contrastive vs. other models under viewpoint shifts (l. 246 etc). However, in Table 2 it is unclear whether these improvements are originating from the improved OoD, or in fact the difference in InD performance. Given that the authors computed error bars for all results, I propose to equip every statement in the paper with a suitable hypothesis test capable to test the influence of the different factors.\n- Another major weakness is the missing link to published results. All models in the paper are trained from scratch by the authors, and no numbers are reported by applying existing model checkpoints from the literature and confirming that the trained models reach a comparable performance level. What is the rationale for not verifying model performance e.g. against the released best models trained on image datasets?\n\n\nOther weaknesses:\n\n- In the training protocol, parameters like the batch size were kept constant across methods. However, it is known that methods like SimCLR depend on availability of large batch sizes, while models like MoCo have mechanisms build in to more effectively leverage data in small batchsize training setups. Hence, I disagree with the author's statement that the considered setting is \"fair\". Happy to discuss the rationale for these choices (vs. for example using the best available model configurations). Minimally, it would be good to discuss this point more in the paper, e.g. in the limitation section.\n- For a purely empirical paper, I would recommend to back up the fairly general statements at the end of each section with statistical tests.\n- The plots are unreadable without heavily zooming in, e.g. Figure 4 and Figure 6. Figure 5 is borderline.\n\nQUESTIONS:\n- If I read the methods correctly, you trained all models from scratch on the respective datasets (Kinetics400 and -700). Did the video models  you trained readily outperform the best \"static image\" models available for the respective methods?\n- Will you open source the model checkpoints and source code for all models you trained in this study? The paper only makes a statement regarding the code.\n- The results in Table 2, Viewpoint (ego) seem to be very bad (around 11%). Did you investigate possible causes for this performance drop? How do you justify to infer conclusions from this part of the table (e.g. in l. 246 etc) given the bad InD performance to begin with?\n\nLIMITATIONS:\nYes, limitations have been addressed (up to the additional comments I made above).\n\nHowever, given the amount of datasets used in the paper, I am missing license and copyright statements for these datasets, e.g. in the appendix, that go beyond the citations provided in section 5 in the main paper. This could e.g. be included into Appendix C. If the benchmark is intended for later release as outlined, I think it would be very useful to have such an overview directly in the paper for future reference by users of the benchmark.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-07"}

comments: SUMMARY:
This works studies various video SSL methods under distribution shifts. 6 different video SSL methods (SiMCLR, MoCO, BYOL, SimSiam, DINO, MAE) are trained on Kinetics-400 under the same experiment settings (e.g. ViT-B, fixed number of epochs, etc) and then evaluated on various distribution shifts (e.g. viewpoint shift, context shift) and report the findings (e.g. contrastive methods more robust to viewpoint shifts, while MAE is a stronger temporal learner).

STRENGTHS:
1. Writing and presentation is clear.
2. Same experimental and test bed setup for all the baselines. The test bed and evaluation code could be useful to the community.
3. Useful empirical observations characterizing the strengths and weakness of prominent approaches.

WEAKNESSES:
No experiments or analyses on model scaling or different architectures. 

Some findings are pretty much expected (e.g. contrastive methods not being good temporal learners happens by design), but it's nice have an easy test bed to quantify them.

Some of the settings controlled for can be strong caveats, e.g. fixing the number of epochs can bias evaluations towards methods that converge faster. It is unclear how much of the results hold when models used their respective optimal settings - which is an important setting for evaluations.

Some of the differences could stem not from core differences in pretext task, but in use of a teacher. e.g. MAE can also use an EMA teacher and the representations would converge much faster.

QUESTIONS:
Could the authors please discuss some of the limitations and caveats to their findings (e.g. some are listed above)?

LIMITATIONS:
Yes.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-07