ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning
Authors: Aman Anand
Amir Eskandari
Elyas Rashno
Farhana Zulkernine
Venue: Accepted by TMLR ()

Abstract: Self-supervised learning (SSL) has shown remarkable success in skeleton-based action recognition by leveraging data augmentations to learn meaningful representations. However, existing SSL methods rely on data augmentations that predominantly focus on masking high-motion frames and high-degree joints such as joints with degree 3 or 4. This results in biased and incomplete feature representations that struggle to generalize across varied motion patterns.  To address this, we propose Asymmetric Spatio-temporal Masking (ASMa) for Skeleton Action Representation Learning, a novel combination of masking to learn a full spectrum of spatio-temporal dynamics inherent in human actions. ASMa employs two complementary masking strategies: one that selectively masks high-degree joints and low-motion, and another that masks low-degree joints and high-motion frames. These masking strategies ensure a more balanced and comprehensive skeleton representation learning. Furthermore, we introduce a learnable feature alignment module to effectively align the representations learned from both masked views. To facilitate deployment in resource-constrained settings and on low-resource devices, we compress the learned and aligned representation into a lightweight model using knowledge distillation. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that our approach outperforms existing SSL methods with an average improvement of 2.7–4.4 % in fine-tuning and up to 5.9 % in transfer learning to noisy datasets and achieves competitive performance compared to fully supervised baselines. Our distilled model achieves 91.4 % parameter reduction and 3× faster inference on edge devices while maintaining competitive accuracy, enabling practical deployment in resource-constrained scenarios.

Published in Transactions on Machine Learning Research (01/2026)
ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning
Aman Anand
aman.anand@queensu.co
School of Computing Queen's University
Amir Eskandari
aniir.ekknndrri@queeee..cc
School of Compuling Queen's University
Elyas Rahsno
elyos. www..roooooouuess.......
School of Computing Queen's University
Farhana Zulkernine
School of Computing Queen's Universily
Reviewed OpenReview: https: // openreview. net/ forum? id=kIFo1g3WS
Abstract
Self-supervised learning (SST.) has shown remarkable success in skeleton-based action recog- nition by leveraging data augmentations to learn meaningful representations. However, existing SSL methods rely on data augmentations that predominantly focus on masking high-motion frames and high-degree joints such as joints with degree 3 or 4. This results in biased and incomplete feature representations that struggle to generalize across var- ied motion patterns. To address this, we propose Asymmetric Spatio-temporal Masking (ASMa) for Skeleton Action Representation Learning, novel combination of masking to learn full spectrum of spatio-temporal dynamics inherent in human actions. ASMa ploys two complementary masking strategies: one that selectively masks high-degree joints and low-motion, and another that masks low-degree joints and high-motion frames. These masking strategies ensure more balanced and comprehensive skeleton representation learn- ing. Furthermore, we introduce a learnable feature alignment module to effectively align the representations learned from both masked views. facilitate deployment resource- coistrained settings and on low-resource devices, we compress the learned and aligned rep- resentation into & lightweight model using knowledge distillation. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that our approach outperforms existing SSL methods with an average improvement of 2.7-4.4% in fine-tuning and up to 5.9% in transfer learning to noisy datasets and achieves competitive performance compared to fully supervised baselines. Our distilled model achieves 91.4% parameter reduction and 3x faster inference on edge devices while maintaining competitive accuracy, enabling practical deployment in resource-constrainee scenarios.
1
Introduction
Skeleton- basdd human action recognition has gained significant attention in human-machine interaction in various areas such healthcare (Ren et al. 2024), security (Liu et al.) 2024 Ruiz-Santaquiteria et al. 2024

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Published in Transactions on Machine Learning Research (01/2026)
klcking something
2
10
13
2010 SITEIA 2003 DISEICRIOOONN
2537 SE DOOEE 2200110000
23
11
writing
14
18
24
19
(a)
(b)
Degree 3,
Degree 1,2
Figure 1: (a) Skeleton graph with joints color-coded by degree centrality. (b) Bar plots show the average motion intensity of each joint for different actions across 50 frames. Through this analysis, we observe that low-degree joints and their adjacent joints exhibit high motion across actions.
and sports analysis QQi 2018). A skeleton is a simple representation of the human body using a graph structure, where nodes represent the body joints and edges represent the bones. Tn recent years, many graph-based supervised learning approaches have been introduced for action recognition such the spatio- temporal graph convolution network (GCN) (Yan et al.) 2018), and Dynamic GCN (Wang et al. 2019). Although fully supervised frameworks have shown great promise, they rely on human-annotated da (astssss. To address this, researchers have proposed self-supervised learning (SSL) frameworks (Zbontar et al. 2021
Grill et al., 2020; Oquab et al. 2023 He et al. 2022 Chen et al., 2020a He et al. 2020 to learn meaningguu
representations from the data and achieved comparable performance to supervised frameworks. Compared conventional RGB video-based representations, skeleton-basee learning offers several advantages. Skeleton data computationally efficient due to their low-dimensional joint coordinates, robust to variations in lighting, viewpoint, background, and explicitly encode molion dynamics through inter-joint relationships. These properties make skeleton representations particularly suitable for efficient and interpretable action understanding in real-world scttings.
Traditional SSL frameworks locus on RGB images and formulate the pretext (Zbontar al. 2021 Grill al. 2020 Oquab al. 22023) task as learning similar representations for differently augmented views of the same image predicting masked patches (He et al., 2022) of the image. By adopting these data augmentations,
recent studies introduced innovative approaches (Zhou et al. 2023 Lin et al. 2020 Yan et al. 2023 to capture spatio-temporal relationships in skeleton data. In general, skeleton-basn
SSL methods leverage data augmentation such as masking sets of body joints to learn spatial information and random video frames to understand temporal relationships. While effective, these augmentations often emphasize limited body regions, potentially overlooking the full spectrum of spatio-temporal dependencies necessary for generalizable action recognition. The solective masking can lead biased representation lcarning which reduces the generalizability of the model in varying motion patterns across different actions.
Through simple analysis illustrated in Figure of connected nodes) and motion intensity (average motion of 50 frames measured from joint displacement) across different actions. The High-degree joints, such as the spine (joints and 21), along with adjacent joints, act as structural stabilizers and exhibit minimal motion. In contrast, the low-degree joints, along with some adjacent joints, show significantly higher motion. This obscrvation suggests that balancod approach to skeleton representation learning should incorporate both types of motion dynamics enhance model generalization.
observed the contrasting role of joint degree (number
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Based on this observation, we aim to learn effective skeleton representations by designing masking strategy that balances the dynamics of both high-degree joints with low motion and low-degree joints with high motion. We propose an Asymmetric Spatio-temporal Masking (ASMa) for skeleton action representation learning, where we train the two ST-GCN-bascd encoders with distinct masking strategies. In addition, we introduce a feature alignment module to effectively combine the diverse representations learned by both encodors for downstream tasks. Inspired by the success of the Barlow Twins SSL framework (Lin et al. 2020), we design our model be a triplet-stream architecture comprising an anchor, spatial, and a temporal stream that shares common ST-GCN backbone as encoder and Barlow Twins head to learn a cross-correlation matrix between the anchor and the other streams. All three streams are learned simultaneously. While the two encoder architectures lead to richer and more diverse representations, it comes with an inevitable cost of higher inference-ting latency, and memory usage, which require more resources and limit deployability on edge devices. To mitigate this, we propose a lightweight model that learns from the combined representation via knowledge distillation (KD). We then evaluate both encoders with the feature alignment module on NTU60 (Shahroudy et al. 2016). NTU120 (Liu et al. 2019), and PKU-MMD {Liu et al. 2020a) datasets for action classification downstream task. To summarize, our main contributions are follows.
1. We propose ASMa, a novel masking technique that enhances skeleton- self-supervised representation learning by applying two complementary masking strategies: one focusing on high-degree joints with low motion and the other focusing on low-degree joints with high motion.
2. We introduce a feature alignment module that effectively integrates the representations learned from the above two masking strategies. Through extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD, we demonstrate consistent performance improvements over prior SSL methods, achicving 1 3% gains in linear probing, 4.44.%% in fine-tuning, and 4 6% in transfer learning settings.
3. We also propose a lightweight model using knowledge distillation (KD) that significantly reduces compu- lational complexity while maintaining competitive performance. Additionally, we uncover novel insight where a student distilled from a lincar-probed teacher can surpass the performance of the teacher itsolf, which demonstrates that generalizable representations can leverage self-supervised distillation.
The rest of the paper is organized
follows: Section 2 reviews related work, Section 3 presents our ASMa methodology including asymmetric masking and feature alignment, Section 4 describes datasets and imple- mentation details, Section 5 provides experimental validation and ablation studies, and Section 6 concludes with future directions.
2 Related Work
2.1 Skeleton-Base Action Recognition
Early methods for skeleton- based action recoguition relied on handcrafted features or statistical models like HMMs (Xia et al. 2012) and Lie group representations (Vemulapalli et al. 2014). but lacked generalization. Deep learning approaches using RNNs, CNNs, and LSTMs improved temporal modeling (Ke al.) 2017). though struggled with long-range dependencics. Graph convolutional networks (GCNs) offered structural advantage, with ST-GCN (Yan el al. 2018) modeling spalio-temporal relationships over skeleton graphs. Follow-up works improved this with adaptive graphs [Chen et al.) 2021), motion shifts (Cheng et al. 2020
and disentangled convolutions (Duan et al. 2022
primarily in supervised settings.
2.2 Sele-Supe Representation Learning
Self-supervised lcarning (SSL) has shown promises in visual domains by learning representations from un- labeled data. Early pretext-lask-basee methods predicted rolations (Gidaris et al. 2018), spatial context [Nooozii et al. 2017), or masked patches (Pathak et al.) 2016). Contrastive methods like MoCo (He et al, 2020) and SimCLR (Chen et al. (2020b) improved feature quality by comparing positive and negative pairs, while BYOL (Grill al. 20200 and SimSiam (Chen & He (021)) removed the need for negatives. Bar- low Twins (Zbontar et al. 2021 further simplified training by encouraging cross-correlation between views.
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Masked modeling techniques like MAE (He et al. 2022 shifted focus to reconstruction-baseed SSL, effective for modeling spatial and temporal structures.
2.3 Self-Supervised Skeleton Representation Learning
Recent SSL approaches for sxeleton-based data adapt visual SSL ideas to motion signals. Contrastive frameworks like CrosSCLR et et al. 2021) and AimCLR (Guo et al.) 2022 leverage inter-view and motion- invariant learning, but often require large memory banks or batch sizes. Negative-sample-tree approaches such (Moliner al. 2022) and PSTL (Zhou al.) 2023) extend Barlow Twins to skeletons, using triplet streams and partial masking. However, many of these methods bias learning loward high-motion or high- degree joints, potentially neglecting complementary body dynamics. Masked modeling has recently gained attention in this domain. SkeletonMAE. (Yan et al. 2023 reconstructs missing joints, but focuses mainly on spatial masking and does not explicitly model motion dynamics. Our work differs by proposing an asym- metric spatio-temporal masking strategy guided by joint degree and motion statistics. Unlike prior works, ASMa encourages learning from both dynamic and stable joint patterns, promoting balanced representation learning.
3
Methodology
3.1
Overview
Figure 2 presents an overview of our proposed ASMa framework, which consists of three stages: (i) Pre- training, (ii) Downstream Evaluation, and (fii) Knowledge Distillation. In the pretraining stage, employ two ST-GCN-based encoders, fo and fe. each trained using distinct asymmetric spatio-temporal masking strategies. Each encoder processes three streams, namely anchor, spatial, and temporal, simultaneously after applying augmentations and masking as shown Figure and 2(c). During downstream evaluation, a feature alignment module fuses the representations learned by fe and fo into a unified embedding for clas- sification. To support efficient deployment, lightweight. student encoder fs is trained to distill knowledge from the frozen ASMa teacher.
3.2 Masking Strategy
We design asymmetric masking strategy that operates along both spatial and temporal views of skeleton sequences. All masking strategics are illustrated in Figure aa) and defined as follows:
Notation. A skeleton sequence is represented as x E RCTXXV where C is the number of channels (typically 3 for 3D coordinates), T is the number of temporal frames, and V is the number of joints. We apply random augmentations T (e.g., crop, rotation, flip) to produce views x' and &, which are then masked to produce spatial and temporal views.
3.2.1 Spatial Masking (Joint Selection)
High-Degree Spatial Masking (HDSM). To prioritize the skeleton joints with high degrees joints (e.g. spinc) in the skeleton to be maskod, we define the masking probability pe for each joint e proportional to its degree centrality d,
do
H p
(1)
Ev= d
where d, is the degree of joint and the superscript (H) denotes high-degree joint masking. Joints are (H) sampled for masking according to pe to produce the joint masked view x/.
Low-Degree Spatial Masking (LDSM). To capture complementary dynamics from peripheral joints (c.g., hands, feet), we define:
((()) 1 p/ (B)
(2)
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Published in Transactions on Machine Learning Research (01/2026)
Masking Strategy
(b) Data Augmentation and Asymmetric Masking
Spatial Masking
Masked
Temporal Masking
Asymmotic Spatio- Temporal Masking
HDSM
LDSM
Masked
(1) Spatial Stream
LOSM
Masked HMTT
Input
LMTTM
Temporal
(c) Pretraining fa j
(d) Downstream Evaluation
(e) Knowledge Distillation
ASMa
C
h
fo
Feelure Alg Mormertt
Linear Clazsifer
z
fu
Teacher
Student
h
fo
Z
& {0,
Overview of the ASMa framework. (a) Shows High-Degree Spatial masking (HDSM) and Low- Degree Spatial masking (LDSM) for joint and High-Motion Temporal masking (HMTM) and Low-Motion Temporal masking (LMTM) for frames. (b) Inputs are split into 3 streams with random augmentation T and masked asymmotrically along spatial and temporal stream. (c) Each encoder processes the triplet streams and trained using Barlow Twins loss. (d) Learned features from both encoders are fused via a feature alignment module for downstream evaluation. (e) JSMa-Distill. lightweight student model learns from the frozen ASMa. teacher logits.
Figure 2:
where the superscript (L) denotes low-degree, joint masking. This prioritizes the masking of low-degree joints, producing the masked view <...
3.2.2 Temporal Masking (Motion-Based Frame Selection)
To select informative or subtle motion patterns over time, compute motion scores across frames. for each frame t, we define motion magnitude the average joint displacement:
First,
(t) || zell,
E CXV
(3)
We then compute motion scores by averaging over all channels and joints:
0, C.V m!()).
(4)
High-Motion Temporal Masking (HMTM). To mask high-motion frames, we select the top-k frames with the largest motion scores d,, denoted TopK(a, k), where TopK returns the indices of the largest elements in a. The corresponding frames in & are masked to produce xt HMTM( (a,
Low-Motion Temporal Masking (LMTM). Conversely, to encourage learning from low-motion frames, we mask the bottom-k frames with the smallest motion scores, denoted as BottomK((, k), where BottomK returns the indices of the & smallest elements a. This yields the masked view = LMTM(@, BottomK(a, k)).
To this end, we obtain two asymmetric augmented views: (z), an), and (x)) x along with the shared unmasked anchor input z. These views serve as inputs the corresponding anchor, spatial, and temporal stream during pretraining.
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3.3 Pretraining fo and fo
As discussed in Section [ we observed that correlation exist. between the degree of joint and its cor- responding motion. We leverage this insight by pretraining two ST-GCN-based encoders, fe and fe, with asymmetric spatio-temporal masking strategies. Each encoder processes three parallel streams: the anchor stream encodes the unmasked input I, the spatial stream encodes the joint-masked view uf, and the temporal stream encodes the motion-masked view z where &o {0,6). The resulting feature embed- dings from these streams are denoted as h² hy, and h respectively, and are fed lo share Barlow Twins projector head p(-) to obtain projected embeddings Z ZJ, and Z
Loss Function. To align the anchor embedding with each masked stream while minimizing redundancy, we use the Barlow Twins loss function (Zbontar et al. 2021 For pair of projected embeddings z, z' E R BxD the cross-correlation matrix C E R DDD is computed as:
Σb= Zb,ii Zb.
Cij
B L==
This
V V (bh3)
where B is the batch size, D is the feature dimension, and i,j (1....;D) D} denote the feature indices in the projected embedding vectors and z', respectivcly.
To align the anchor stream embedding Zt with the spatially masked stream z for encoder fx (where k {0, Φ)), we compute the Barlow Twins loss C as:
Σ (1 ==1
C +
(
(6)
where C* E RDD is the cross-correlation matrix between projected anchor embeddings Z and spatial embeddings Z and a regularization coefficient.
Similarly, to align the anchor embedding z with the temporally masked stream Z we compute the loss C as:
D
c Σ (1-c))) 1=1
C% +
2
(ct)
(7)
3
where C e DxD is the cross-correlation matrix between z and Z The total loss for encoder fa then the sum of the two alignment losses:
Clotal c +
(8)
and the final pretraining objective across both encoders becomes:
LASMa Clocal Giotal
(9)
This objective encourages both encoders to learn diverse yet complementary representations aligned across masked and unmasked spatio-temporal viows.
3.4 Feature Alignment
For downstream evaluation, we align and integrate the diverse representations learned by fo and fo using bi-dircctional cross-attention mechanism. Let h E BxND and he e BBNND denote the feature embeddings extracted from fo and fe. respectively, where is the batch size, N is the sequence length (frames), and D is the embedding dimension.

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To align h" and h apply Multi-Head Attention (MHA) in bi-directional manner: fo attends to for and vice versa. The attention mechanism follows the standard scaled dot-product attention Vaswani 2017
QK Vdz
At K, Softmax
V,
(10)
where Q, K, and V the query, key, and value matrices, and dg the dimension of the keys. Specifically,
compute two attention flows where, fo attends to fa by setting Q h K = V = h producing hg alligned
and fa attends to fo by setting Q = h K = = h², producing h Daligned
We then fuse the outputs as shown in Eq. 11
haligned aligued + h e
(11)
aligned
The resulting unified representation haligned is passed through a linear classification head:
y W cls haligned
(12)
where Wels DDCC is the classification weight matrix, and C is the number of action classes. The full feature alignment module, including the MHA blocks and classifier head, is trained end-to-end using standard cross- entropy loss. We use 4 heads in MHA in this module.
3.5 Knowledge Distillation
To enable efficient. deployment, we distill the unified representation learned by the feat ure alignment module into a lightweight student encoder fa. This step reduces model size and inference cost while aiming retain most of the performance achieved by the full ASMa framework. We design teacher-student distillation framework, where the teacher is defined as the full model comprising fa, fa, and the feature alignment mod- ule. During training, the teacher model is kept frozen to prevent collapse and to provide stable supervision. Both the teacher and the student fa take the same imput skeleton sequence and produce logits Za. and 2, respectively. To facilitate effective distillation, we apply temperature scaling to the teacher logits. The softened distribution over classes computed as:
T
(13)
Σ ES eFn
where i is the i-th component of the teacher logits Zg. 7 is the temperature paramcter, C is the total number of classes, and i indexes the class dimension. Temperature scaling helps preserve class-level similarity by smoothing the distribution. The student output is similarly converted into a probability distribution y using softmax. We minimize the Kullback-Loible (KL) divergence between the student and teacher distributions:
Lud = CKL(SS
(14)
4 Datasets and Implementation
Datascts. We evaluate ASMa on three standard benchmarks: NTU RGB+D 60 (Shahroudy al. 2016) NTU RGB+D 120 {Liu et al. 2019), and PKU-MMD (Liu al. 2020a). NTU-60 consists of 56,578 skeleton sequences from 60 actions across 40 subjects; use the standard cross-subject (xsub) and cross-view (xview) splits. NTU-120 extends this with 113,945 sequences across 120 actions and 106 subjects, evaluated under cross-subject (xsub) and cross-setup (xset) protocols. PKU-MMD contains 28,443 sequences (Part I and IT combined) covering 51 actions. We use Part. for pretraining and both parts for transfer evaluation. PKU-MMD presents significant challenges due to viewpoint variation and skeleton noise in Part II.
Implementation Details. For all experiments, we use the ST-GCN (Yan et al. 2018 backbone with 16 hidden channels and train with Adam optimizer and CosineAnnealing scheduler for 150 epochs. Batch size

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is set to 128. We pretrain fo and fo on the xsub splits of NTU-60 and NTU-120, and on Part I of PKU- MMD. We mask 9 joints and 10 frames during pretraining, based on the ablation shown in Appendix A.2 Each encoder outputs a 256-dimensional vector, projected to 6144-dimensional space. The loss trade-off parameter À is set to 2 x 10-4, and weight decay is 1c-5 with 10 warmup epochs.
Downstream Evaluation. We evaluate pretrained models on action classification via (i) Linear Evaluation and (ii) Fine-tuning. In linear evaluation, we freeze fa and fo and train only the feature alignment module and classification head for 150 epochs with learning rate of 1c-3. We also report lincar-probing results from individual encoders. In fine- tunnttnnng,
train each encoder end-to-end with linear head for 150 epochs at a 5e-3 learning rate. For the combined fine-tuning, we attach our feature alignment module on top of fine-tuned Sa and fe and train it for 50 epochs with a learning rate of le-4.
Knowledge Distillation. The student model f. consists ST-GCN layers with spatial kernel size 3 and temporal kernel size 9. is trained using KL divergence loss (without ground-truth supervision) for 150 epochs al a learning rale of le-2.
5 Experimental Validation
Our experimental validation addresses the following questions:
RQI Action Classification & Efficiency: How do (three-stream) 3s-ASMa and 3i-ASMaDDistill compare with supervised and self-supervised baselines under linear evaluation and fine-tuning, and what accuracy efficiency trade-offs do they offer (including on-device performance)?
RQ2 Ablations Study: What are the individual contributions of the two encoders (fe, fo), and how much does the feature aligument module help? How do spatial temporal masking pairings behave, and does asymmetric masking outperform synmetric/raadoon choices?
RQ3 Distillation Sensitivity: How sensitive is ASMa-Distill to distillation hyperparameters, particularly temperature 7 and the student's depth (number of ST-GCN layers)?
RQ4 Linear Probed vs Finetuned KD: How does the linear-probed vs. fine-tuned affect student performance and the quality of learned representations?
RQ5 Transfer Learning Evaluation: How well do ASMa's representations transfer to noisy datasets?
5.1 RQ1: Action Classification and Efficiency
In Table ! we report the performance of both our proposed As-ASMa and its distilled variant, ASMA Distill, alongside statc-of-the-art supervised and self-supervised methods. The table presents linear evalua- tion (Lin.) and fine-tuned (FT.) top-1 accuracy across NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD, together with efficiency metrics including parameter count and FLOPPs The prefix 3-Stream (3s) denotes that results are obtained by combining the joint, bone, and motion streams.
Discussion. From Table ! several key observations emerge. First, AS-A achieves consistent improve- ments over prior self-supervised learning approaches, surpassing them by up to 4-6% in fine-tuning accuracy across NTU-60, NTU-120, and PKU-MMD. These gains highlight the importance of our asymmetric mask- ing strategy for learning richer skeleton representations and the role of the feature alignment module in enhancing downstream generalization. Second, while ASMa establishes new state-of-the-art benchmark, we recognize that deploying such models in edge scenarios is challenging duc to their size and computational overhead. To address this, we introduce 3s- AS---MiD-istill, a lightweight student model distilled from ASMa. Despite having only 0.54M parameters and 1.26G FLOPs, the distilled model not only preserves strong accuracy but also outperforms all previous basclines on NTU-60 and NTU-120. These results demonstrate that our framework delivers both state-of-the-art accuracy and practical efficiency, making suitable for real-world deployment. on resource-consfrainne devices.
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Table 1: Comparison action recognition results on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD with efficiency metrics. Results are reported for linear ovaluation (Lin.) and fine-tuning (FT.). Params (milliinn) and FLOPs (G) are included where available. Backbone (Bk.) identifiers: (1) ST-GCN, (2) GCN, (3) CNN, (4) DGCNN, (5) STTFrrmer, (6) Transformer, (7) Transforeer..
60
120
PKU-MMD
Method
Bk. Params
FLOPs
xview
xsub
Part
Part II
Lin.
FT.
Lin.
FT.
Lin.
FT.
Lin.
FT.
Lin.
FT.
Lin.
FT.
:ally-Supervised:
ST-GCN Yan el al.
2018
1
81.
88.3
2s-AGCN (SD
2
88.5
95.1
82.9
81.9
ShiftGCN Cheng
2020
2
90.7
96.5
85.9
87.6
GSS [La
2020
91.5
96.2
86.9
88.4
PYSK1, Dian
93.7
96.6
86.0
89.6
CTR-GGN
Chen et
2021
2
92.4
96.8
88.9
90.6
BlockGON
93.1
91.5
HiOD Chang et al
2
93.8
97.6
90.4
91.9
sele-Superviied:
3e-SkeletonBTT
Zhou et al.
2023
69.3
72.2
36.3
56.8
81.8
11.1
3e-SkeletonCI
75.0
62.6
Bc-TT-GN Yan ct at.
2018
85.2
91.4
77.2
77.1
Cr.CSSSCLLRR
112T
17.28
77.8
86.2
67.9
67.1
80.4
21.2
SkeletonMAE
Wi
21/23
86.0
92.9
76.8
79.1
Ai-AiCL.. (Guo at. 20222
2.85
3.45
78.9
86.9
83.8
92.8
68.2
80.1
68.8
80.9
87.8
38.5
3e-colorization Yang,
2221
75.2
88.0
83.1
3e-PSTL (Zhou et al. 2025
2.85
3.45
79.1
87.1
83.8
93.9
69.2
81.3
70.3
82.6
89.2
52.3
69.1
SCD-Nel [Nu
54.0
IGM [Län of al. 20125a
86.2
91.2
80.0
81.4
ActCEi Lan a. 2025b
2
89.0
94.2
82.2
84.8
91.6
62.1
STJD-CI. Gumasolara
2025
89.3
94.8
83.5
86.8
93.2
55.3
As-ASMa
6.3
4.5
87.3
92.0
91.9
96.8
80.1
87.9
81.0
88.8
94.5
68.9
76.8
0.7
2.7
0.2
1.9
3.2
1 4.3
0.0.9
2.0
1.3
14.9
7.7.
35- As-aa-lDl
0.54
1.22
91.7
86.9
88.3
75.8
191.4%
472%
2.4
3.4.
1.5
0.7
Table 2: Accuracy and inference efficiency comparison between 3s- ASMM and 3s- As-ASMa-Distilll on Raspberry Pi (2GB RAM, CPU only).
Model
NTU60 (FT)
NTU120 (FT)
Params(M)
Size(MB)
FLOPs(G)
Time(ms)
Mem(MB)
FPS
ssub
xview
xsub
xsel
ASMA (Teucher)
92.0%
98.8%
87.9%
88.7%
6.3
25.5
4.5
39.16
314.47
16.00
3s ASMM- Distill
91.7%
95.9%
80.9%
88.3%
0.54
2.22
1.28
21.38
173.28
46.52
Performance/ /Etheeer
10.3%
10.9%
11.0%
491.4%
J91.20 201.2%
172%
163.8%
J44.8%
163.0%
On Edge Performance: To assess practical applicability, we benchmark ASMa-Distill on a Raspberry Pi 4B (2GB RAM, dual-core CPU). As shown Table 2 the student model achieves only 0.65% average performance drop while reducing parameters by 91%, disk footprint by 91.2%, and FLOPs by 72%. It runs 3x faster (21.38ms 59.16ms), uses 45% less memory, and reaches 46.52 FPS, demonstrating feasibility for real-time edge deployment.
5.2 RQ2: Ablation Study
We perform an ablation of the two encoders fo and fe and the feature- alignment module. Both encoders use the same ST-GCN backbone and training setup; they differ only in their masking stralegy. Thus, their comparison rellects the effect different spatio-temporal masking configurations rather than archi- tecture. As shown in Table each encoder captures complementary motion cues, and their combination improves accuracy by 1 1-2% across datasets.
Table 3: Ablation study of individual encoders and the feature alignment (FA) module.
Model
NTU60 xsub
NTU120
PKU-MMD
xview
xsub
xsel
Part I
Part II
So only fo only
83.8
88.2
75.1
75.9
91.0
64.3
84.1
89.1
76.0
76.8
91.7
65.1
Ja (w/o FA) fa (w/ FA)
85.9
90.5
78.3
79.0
93.6
68.2
87.3
80.1
81.0
94.5
68.9

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Published in Transactions on Machine Learning Research (01/2026)
Removing the feature-aligument module and averaging encoder outputs lowers accuracy by up to 2%, con- firming the benefit of adaptive fusion.
To study how spatial and temporal mask- ing interact, we ablate ASMa on the joint stream using all pairwise combinations of Spatio-temporal masking as shown in Fig- ure 3
NTU60 (xsub)
NTU60 (xview)
Ranson
74.10
74.28
73.36
79.33
79.50
78.70
Spetial Masking
74.25
74.10
74.40
80.61
Discussion. Across both NTU60 splits (xsub/xview), the best performance is ob- lained when masking low-degree joints with high-motion frames; the second-best comes from the complementary pairing of high-degree joints with low-motion frames (highlighted in the figure). In contrast, symmetric pairings high or low+low) and random masking are consistently weaker. These results align with our observation that high-degree joints typically move less while low-degree joints exhibit greater motion (see Fig. [ and support our design choice of asymmetric spatio-temporal masking in Fig. B By
dagguee
74.30
75.04
70.42
80.40
81.92
75.86
Random
High-motion Low-mmttion
Highmmurien Low-motion Temporal Masking
Temporal Masking
Figure 3: Ablation on different, pairs of masking strategies. Refer to Appendix B.1 for other benchmarks.
cross-pairing spatial and temporal difficulty, the pretext task exposes complementary cues and encourages more discriminative representations, providing direct empirical evidence for our claim.
To evaluate the architectural flexibility of ASMa, we conduct experiments using three popular backbone networks: Graph Isomorphism Network (GIN), Dynamic Graph CNN (DGCNN), and Spatio- Temporal GCN (ST-GCN), as shown in Table 4 While ASMa demonstrates con- sistent gains across all three backbones un- der both lincar evaluation and fine-tuning
Table 4: Performance of ASMa across different backbones (Bk).
NTU60
NTU120
Bk
xsub
Xview
||
xsub
Xset
Lin.
FT.
Lin.
FT.
Lin.
FT.
Lin.
FT.
GIN
75.23
82.43
81.34
91.05
64.47
75.16
65.87
77.12
DGCNN
75.10
83.29
81.48
91.30
65.42
75.62
66.94
77.29
ST-GCN
75.84
84.08
81.92
91.53
65.73
76.39
67.08
77.58
protocols, ST-GCN achieves the highest accuracy all splits. This suggests that although ASMa gen- eralizes well to different backbone designs, ST-GCN is particularly effective in capturing spatio-temporal dependencies in skeleton sequences.
5.3 RQ3: Distillation sensitivity
In this section, we discuss the effect of temperature parameter 7 of Eq. 13 the number of ST-GCN layers on the performance of our compact model f.. First, to study the effect of temperature-scaled distillation, keep the number of ST-GCN layers fixed to 5 in fa and vary the value of r.
Discussion. In general, as shown in Figure (Teft), we observe similar performance pattern where too low or high value of r results in performance drop across all benchmarks. However, the optimal value of T depends on the class distribution of the datasets. Specifically, for datasets with fewer classes, such as NTU60 (60 classes), our intuition suggests that the output distributions are inherently sharper and require higher values (e-g., 8,9) for effective softening logits and better distillation. For example, NTU60-xview achieves its highest performance (90.75%)
9. In contrast, datasets with more classes, such NTU120 (120 classes), naturally produce smoother output logits. Excessive softening with higher value of 7 blur critical inter-class distinctions, leading to suboptimal performance. Notably, NTU120-xsub achieves 77.42% accuracy at т = 6, outperforming higher values. Next, we analyze the effect of increasing and decreasing number of ST-GCN layers in fa- As shown in Figure 4 (Right), there is a consistent performance improvement with increasing number of layers. However, increasing the number layers beyond results in marginal improvements on all benchmarks. Specifically, NTU60-xsub improves by only 0.49%, and NTU60- xview gains 0.28% when moving from 5 to layers. However, reducing the number of layers to or 2 leads to noticeable performance drop across all datasets. To this end, we find that 5 ST-GCN layers in fs show
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reasonable trade-off between performance and compression. A detailed comparison between feature-level and logit-level distillation, including implementation and quantitative results, is presented in Appendix
20.75
95
-+- m7.60-cuub
stut20oout
ma.120
50 50.75%
1oot AUCC8)
Top3: AUCOS)
90
0.5
86 64.55
00
80
0,5
78.0%
75
77
75
Tempentture ure
Layers
Figure 4: Left: We keep the number of ST-GCN layer in Is to 5 and vary T to lest lemperature sensilivity of distillation. Right: We vary the number number of ST-GCN layers in fs test compression sensitivity.
5.4 RQ4: Linear Probed Finetuned KD
While temperature scaling is common approach to soften the logits for better knowledge transfer, it is inherently a heuristic method. This raises interesting question: What if distill from linear- probed teacher, which naturally produces softer logits due to its constrained learning setup?
To find the answer, we conduct exper- iments to compare the performance of students when distilled from linear- probed (LP) teacher vs a fine-tuned (FT) teacher. Interestingly, shown in Table 5 the student distilled from the LP teacher consistently outper- forms its teacher on all datasets, with an average performance gain 5.2%.
Table 5: Comparison of distillation from lincar-probed vs finc- tuned ASMa. Refer to Appendix A.3 for other streams.
NTU60
NTU120
Stream
Variant
xsub
Xview
xsub Lin.
Xset
Lin.
FT.
Lin.
FT.
FT.
Lin.
FT.
ASMa (T)
75.8
85.5
81.1
92.6
66.1
79.2
66.9
80.2
Bone (B)
ASMa (S)
80.3
84.6
85.5
90.7
72.0
77.4
73.2
78.8
1/4
14.5
J0.9
14.4
41.9
15.9
11.8
16.3
J1.4
Discussion. To understand why fa per- forms better than the teacher when dis- tilled from LP model, we analyze the SNE embedding projection in Figure 5 We choose all data samples from 9 random classes NTU60-xsub (test set) for clear visualization. As shown Figure 5 LP- distilled student (bottom right) demon- strates more compact and well-separated clusters compared to its teacher (bottom left). In contrast, the FT-distilled stu- dent (top right) shows slightly dispersed clusters compared to its FT teacher (lop left). Intuitively speaking, since the stu- dent has nearly 12 times fewer parame- ters than the teacher, we observe an ag- gregated effect of representation learned by the teacher which leads to more refined and compact clusters in the LP-distilled case. However, in the FT setting, since the decision boundaries are more fine-gre graine, the student struggles to retain the perfor- mance of the teacher due lower capac- ity. Note that although distillation from
ASMa (FT)
ASMs (FT) Distilled
40-
20
-40
-40
-10
-20
ASMs (LP)
ASMa (LP) Distilled
40
20
-20
-40
-40
claoping
tear paper
hatrcap
reading
wear shoe
take off haticep
writing
wear on glasses
walking towerds each other
Figure 5: t-SNE embedding projections of randomly selected classes from NTU60-xsub (test set). Refer to Appendix A. for other classes.
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fine-tuned teacher shows better performance than the linear-probed teacher, it comes with the cost of finding the perfect value of 7 for better distillation. Therefore, in scenarios where training multiple models is not viable due to time constraints, distillation from linear-probed teachers offers a viable solution in case of skeleton representation learning with a reasonable trade-off on performance.
5.5 RQ5: Transfer Learning Evaluation
Table that ASMa achieves notable improvements in transfer learning, outper- forming baselines by 4-6% on PKU-Part- II, dataset known for its high noise levels.
Table 6: Transfer learning performance (FT). Models are pre- trained on NTU60-xsub, NTU120-xsub, or PKU-Part-L and transferred to PKU-Part-II. "idiccaess results reproduced using official code of (Zhou el al. 2023).
This gain highlights the su- perior generalization ability of our model with its asymmetric masking strategy and feature alignment, which enforces learning from both high-degrce joints with low mo- tion and low-degree joints with high mo- tion. By capturing a broader range of mo- tion dynamics, ASMa learns more com- prehensive representations, which makes it better suited for challenging and noisy datasets.
Discussion:
Method
Transfer to PKU-Part-II
NTU60
NTU120
PKU
TGAN (Zheng et
2018
44.8
43.6
MS2L [Lin
2020
45.8
44.1
ISC (T hooker
202
51.1
52.3
45.1
CMD (Mao et al. 2022
56.0
57.0
SkeletonMAA Yan et. al.
2023
58.4
61.0
62.5
S-JEPA Lab
2024
71.4
74.2
3s-PSTL" Zhou al.
2023
72.4
70.1
69.1
3s-ASMa
77.0
76.8
4.8
1.2.8
5.9
6 Conclusion
We proposed Asymmetric Spatio- Tenioo-l Skeleton Action Representation Learning (ASMa), self- supervised learning framework that enhances skeleton-based action recognition by leveraging asymmetric masking in spatial and temporal streams. Our feature aligmment module effectively integrates diverse repre- sentations, and knowledge distillation enables lightweight model suitable for resource-constrained environ- ments. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD show that ASMa surpasses previous sclf-supervised methods and achieves competitive performance with supervised models. Notably, distillation from a linear-probed teacher yields a more compact and generalizable st student model. Future work includes extending ASMa to multi-modal learning and exploring asymmetric spatio-temporal masking to improve the generalization of SSL frameworks. While ASMa achieves strong performance across bench- marks, we acknowledge scope considerations for future extensions. First, like other skeleton-based methods, performance depends
pose estimation quality; however, our strong transfer results on PKU-MMD Part II (Table 6). a dataset with significant noise and viewpoint variation, suggest that the asymmetric mask- ing strategy helps mitigate noise effects by capturing complementary motion dynamics. Second, ASMa is designed for single-person action recognition, consistent with slandard benchmarks, and does not explicitly interactions or occlusions. Extending to multi-person scenarios and further improving noise handling remain promising directions for future work.
model
Broader Impact Statement
Skeleton- baaee action recognition offers privacy advantages over RGB video by reducing personally identifi- able information. However, like all surveillance technologies, it could be misused for non-consensual tracking. Additionally, performance may vary across populations due to dataset biases; practitioners should evaluate fairness across diverse user groups. We encourage practitioners to obtain informed consent, comply with local privacy regulations (c.g., GDPR), and deploy our methods only in approved applications. The datasets used this work (NTU RGB+D, PKU-MMD) are standard public benchmarks collected with participant consent and widely adopted in the research community.
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Acknowledgments
This work was undertaken thanks in part to funding from the Connected Minds program, supported by Canada First Research Excellence Fund, grant #CFREF-2022-0000, This work was also supported by NSERC Discovery RGPIN-2018-05550.
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Appendix
A Extended Experiments and Analysis
Accuracy Difference: VS fg
-0.05
-0.15
-0.30
-5.10
3 Saape
HAl pur eooe
a55 May
pars
SAA scop
aney
Action
Figure 6: Accuracy difference per action class between fo and fa. Positive values indicate actions where fo outperforms fo. while negative values indicate better performance of fo-
A.1 Encoder-wise Performance Analysis
Figure G presents a class-wise comparison of the accuracy difference between the two encoders, fo and fo- The values indicate the relative advantage of one encoder over the other for specific action categories. Positive values show actions where fa achieves higher accuracy, while negative values indicate better performance of fo- We observe that, while fo generally outperforms fa, there are several action categories where fo exhibits superior accuracy. This suggests that fg captures certain muances in the data that fe may overlook. The complementary nature of both encoders highlights the importance of integrating diverse perspectives in skeleton-basee representation learning. Specifically, the presence of actions where Sa excels indicates that relying solely on one masking strategy may result in incomplete feature learning. This reinforces the necessity of aligning the representations learned from both encoders to achieve more comprehensive understanding of human actions.
A.2 Effect of Masking Ratio.
We perform an ablation study on the NTU RGB+D 60 dataset to assess the sensitivity of ASMa to the number of masked joints (n) and frames (k) during pretraining. As shown in Table 7 we observe that performance peaks when n = 9 joints and k = 10 frames are masked. Increasing the masking beyond these values degrades performance, likely due to excessive information loss, while lower masking levels reduce the diversity of views presented during training. These findings empirically support our choice of masking 9 joints and 10 frames during pretraining.
A.3 Distillation Performance
In the main paper, we analyzed the impact of distillation from a linear-probed (LP) teacher versus a fine- tuned (FT) teacher, focusing solely on the Bone stream(Table 5p. We observed that distilling from the LP teacher led to a more generalizable student model, outperforming its own teacher while maintaining compactness. To extend this analysis, Table g presents the distillation results across all streams, including Joint and Motion, in both linear evaluation and fine-tuning settings.
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Table 7: Ablation study on the NTU RGB+D 60 dataset evaluating the impact of varying the number of masked joints (n) and masked frames (k) during pretraining. All results are reported using linear probe accuracy (%) under the ASa setting.
Setting
Mask Count
As-ASMa Accuracy (%)
7
86.1
8
87.1
Masked Joints (n), k = 10 fixed
9
87.3
10
86.5
7
85.2
8
86.1
Masked Frames (k), fixed
86.8
10
87.3
11
87.0
Table 8: Accuracy of Distillation from Linear probed vs Finetuned ASMa on all streams.
NTU-60 (%)
NTU-120 (%)
Stream
Variant
xsub
xvicw
xsub
xSct
Lin.
FT.
Lin.
FT.
Lin.
FT.
Lin.
FT.
ASMa ASMa-Distill
76.5
84.9
83.8
92.3
66.4
77.1
67.5
78.3
Joint (J)
80.5
84.1
86.8
90.5
71.4
76.0
73.2
76.8
Motion (M)
ASMa ASMa-Distill
69.5
82.9
74.7
90.4
58.7
74.0
59.7
74.8
75.5
81.8
80.0
88.3
67.5
73.3
65.9
73.7
Bone (B)
ASMa
75.8
85.5
81.1
92.6
66.1
79.2
66.9
80.2
ASMa-Distill
80.3
84.6
85.5
90.7
72.0
77.4
73.2
78.8
The trends observed in the Bone stream persist across other streams as well. ASMa-Distill surpasses ASMa the linear evaluation setting for all streams, reinforcing the hypothesis that linear proarr teacher provides transferable representation. Notably, in the Joint stream, ASMa-Distill achieves 4-6% gain in linear evaluation, further confirming that LP-trained teachers encode smoother decision boundaries that are beneficial for distillation. Similarly, for the Motion stream, the distilled model exhibits substantial improvements over its teacher in linear evaluation.
Although distillation from a fine-tuned model shows a better performance over distillation from the linear- probed model, comes with the cost finding the perfect value of t for better distillation. Therefore, scenarios where training multiple models not viable due time constraints, distillation from linear-probed teachers offers viable solution with reasonable trade-off performance.
A.4 Additional t-SNE Embedding Projections for Distillation Analysis
To further analyze the effect of distillation from linear-probed (LP) versus fine-tuned (FT) teacher,
provide additional t-SNE embedding projections in Figures 7a and 7b Each figure visualizes 10 randomly selected action classes from NTU60-xsub (test set), offering a broader perspective on the representation learned by different models.
Consistent with the obscrvations Figure 5 from the main paper, we see that the LP-distilled student exhibits more compact and well-separated clusters compared its FT-distilled counterpart. This suggests that distillation from linear-probed teacher, which inherently produces softer logits, helps the student
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Published in Transactions on Machine Learning Research (01/2026)
ASMa (FT)
ASMa (FT) Distlled
ASMe (FT)
ASMa (FT) Diailled
-33
-43
(LP)
Dietiled
ASM (LP)
ASMe (UP) Disilled
-10
take
sholle head falling
(wii hurdd super/fociing touch other persor's pocket
selt
bront
lata off giamss
Crech fine Ilum wotch +bb two hards togetner
white
sneeadioogh
cheeruu:
(a)
(b)
Figure 7: t-SNE embedding projections of 10 randomly selected classes from NTU60-xsub (test set). (a) Random set 1. (b) Random set 2.
network learn more generalizable and structured represenlations. On the other hand, the FT-distilled student struggles to retain the fine-grained decision boundaries of its teacher, leading to slightly more dispersed clusters.
B Ablation on Masking strategy and Prior Frameworks
B.1 Combination of Masking Strategy
To further strengthen the analysis, we evaluate different NTU RGB-D 60, NTU RGB+D 120, and PKU-MMD benchmarks. All experiments use the same ST- GCN backbone and hyperparameters (Adam optimizer, 150 epochs, batch size 128, masking joints and 10 frames). Only the masking configuration is varied. As shown in Table 9 the asymmetric combinations, masking low-degree joints with high-motion frames or high-degree joints with low-motion frames, consistently achieve the best results across all datasets. This emperically validates that the proposed asymmetric masking design generalizes well across benchmarks.
masking combinations across the
Table 9: Ablation study on different combination of spatial and temporal masking strategies, Linear evalu- ation accuracy (%) reported for NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasots.
Masking Strategy
NTU60
NTU120
PKU-MMD
Spatial
Temporal
xsub
xview
xsub
xset
Part I
Part II
Random
Random
71.2
79.3
63.4
61.25
82.92
17.26
Low-Degree
Low-Motion
70.42
75.86
62.34
63.21
81.24
45.15
High-Degree
High-Motion
74.1
80.26
64.05
64.96
84.70
47.93
High-Degres
Low-Motion
74.38
80.61
64.81
65.30
84.90
52.94
Low-Degree
High-Motion
75.84
81.92
66.73
67.80
87.74
54.35
19

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Published in Transactions on Machine Learning Research (01/2026)
B.2 Generalization of ASMa an Augmentation Strategy
To examine the generality of the proposed Asymmetric Spatio-Temporal Masking (ASMa) strategy, we re- trained previous SSL frameworks by replacing only their original data augmentations with ASMa, while keeping all other components, training objectives, and hyperparameters identical to their official imple- mentations. The evaluated methods include LS 3s-AimCLR, and 3s- Di-CrossCLR. Training followed the configurations in their respective papers: MS used multi-task learning with motion prediction, Jigsaw puzzle, and contrastive losses (learning rate 1 x 10-3, batch size 128, SGD, 120 epochs), while 3s- As-AimLLR and 3s- CrosssssLRR used temperature T = 0.07, momentum m = 0.999, memory bank size 65,536, and learning rate 0.1 with CosileAnnealing for 300 epochs. The original augmentations (e.g., Crop in AimCLR, Cross- View rotation CrossCLR) were replaced by ASMa's asymmetric masking.
shown in Table T0 consistent improvements (1.0 2.3%) across all baselines demonstrate that ASMa serves as a general augmentation mechanism enhancing representation learning regardless of the SSL objective. These results highlight ASMa's ability to expose more in- formative spatio-temporal dependencies than standard geometric augmentations.
Table 10: Performance of cxisting SSL methods equipped with the proposed ASMa augmentation. M denotes results reproduced using official code. Values parentheses indicate improvement over the baseline.
Method
NTU60 (x-sub)
NTU60 (x-view)
M
84.6
91.3
M + ASMa
86.0 (+1.4)
93.6 (+2.3)
3s- ALR
86.9
92.8
C Feature vs Logit Distillation
3s- AimCLR + ASMa
88.2 (+1.3)
95.1 (+2.3)
3s- CrossCLR
86.2
92.5
We empirically compare feature-level and logit-level distillation to examine their effect performance and complexity. For feature- level distillation, an additional linear projection layer was added to the teacher to match the 128-D student ionn The model was trained with an T.2-normalized cosine similarity loss for 150 epochs (learning rate x 10-3), As shown in Table I1 after fine-tuning, the student achieved lower accuracy than with logit-level distillation.
3s- CrossCLR + ASMa
87.2 (+1.0)
94.0 (+1.5)
D Statistical Significance Analysis
To evaluate the statistical significance and re- producibility of our results, we conducted 5 independent runs with different random seeds for all 3s-ASMa experiments. In Table [2] we reports mean accuracy and standard de- viation across these runs, comparing with the strongest relevent SSL baselines from Table [
Table 11: Comparison between feature-level and logit-level distillation on NTU60.
Distillation Method
NTU60 (x-sub)
NTU60 (x-view)
Feature-level (Cosine)
82.43
89.00
Logit-level (KL)
84.69 (T = 8.0)
90.75 (r 9.0)
The low standard deviations (0.08 0.25%) demonstrate that all improvements over the strongest SSL base- lines are statistically significant p < 0.01 level.
Table 12: Mean accuracy + standard deviation over 5 independent runs with different random seeds. Base- lines represent the strongest SSL methods from Table 1 that report results in the same evaluation protocol.
NTU60 xsub
NTUG0 xview
NTU120 xsub
NTU120 xset
Mothod
Lin.
FT.
Lin.
FT.
Lin.
FT.
Lin.
FT.
SSL baselines
86.6
89.3
91.7
94.8
76.9
83.5
80.1
86.8
(SCD-Net)
(STJD-CL)
(SCD:Net)
(STJD-CL)
(SCD-Net)
(STJD-CL)
(SCD-Net)
(STJD.CL)
3s-ASMa (ours)
87.2 + 0.12
92.1 4: 0.25
92.0 + 0.10
96.75 + 0.16
80.0 + 0.16
87.92 + 0.13
81.15 + 0.13
88.72 + 0.21
Improvement
-0.6
+2.8
+0.3
+1.5
+3.1
+4.42
+1.05
+1.92
20

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Published in Transactions on Machine Learning Research (01/2026)
E
Pseudocode
Algorithm ! provides concise overview of the complete ASMa framework. The algorithm consists of three key components: (i) computing masking probabilities based on joint degree and motion intensity (Lines 2 4), (ii) pretraining two encoders So and fe with complementary asymmetric masking strategies using Barlow Twins loss (Lines 5 5-17), and (iii) downstream evaluation via feature alignment and optional knowledge distillation for model compression (Lines 18-21). Each step corresponds the equations presented facilitating reproducibility of our approach.
Section 3
Algorithm ASMa: Asymmetric Spatio-temporal Masking
Input: Skeleton sequence x E RCxIxV
joints, k 10 frames
Compute Masking Probabilities:
Pe (H) d./ E dus Pv 1
Pe for U VEV V
(H)
Eq.
m(t) = [ xx a C.V Ec,o mt(t) for t t
Eq.
Create Masked Views:
x' T(x), & T(x)
x LDSM(X' p(4) n), x5 f- HDSM(X', ( p((),)
8: x + HMTM((,, TopK(a, k)), x + LMTM(($, BottomK(a, k))
9: Encode & Compute Loss:
10: for h (0,
o) do
11:
h h h
Sx(x), fx(x))),x((x
Z ", De + ((hh)).((i)) ((b))),(h()))
12:
13:
C² CrossCorr(Z*' - Č* CrrssCCor(ZZ* 2
Eq.
14:
C (1 t +>E (C
Eq.
15:
C = E, C+) + +> #イ (Cty
Eq.
16: end for
17: LASMa = (C + C9) + (C + C
Eq.
18: Downstream Feature A Alignment:
19: haligned MHA(h", h h²) + MHA(h h² h
Eq.
10
20: ÿ y=Wlss haligned
Lqq
12
21: Knowledge Distillation:
22: 0f = @t.,T/ EE ERkall, Lika (T((5>1577)
Eq..
13 14
21


Reviews:
[{"review_id": "e2uR8zHV06", "reviewer": "Reviewer_DgRW", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper tackles (human) action recognition from sequences of skeletal graphs representing a human’s pose. This is done by training an encoder in a self-supervised manner to encode such a sequence into a latent representation, so that a classifier can predict from those latents an action class. This paper extends earlier work and achieves both accuracy improvements and a reduction in computational cost of the model.\n\nEarlier work by (Zhou et al. 2023) had introduced a Barlow twins-like self-supervised training approach for the encoder. The idea was to take three streams of skeleton graph sequences and feed them through the same encoder: (1) the original stream (2) a stream masked in the temporal dimension (some frames masked out) and (3) a stream masked in the spatial dimension (some nodes in the skeleton graphs masked out). The encoder was then trained to produce maximally correlated latents between these streams, and uncorrelated latents with other streams.\n\n(Zhou et al. 2023) used non-uniform masking: they masked high-degree joints (nodes; spatial dimension) in the skeletal graph with higher probability, as their connectedness allows them to acquire richer neighbourhood information, and masked with higher probability key-frames (temporal dimension) with higher motion. Based on analysis of existing data this paper points out that high-degree joints tend to experience lower motion. They therefore propose what they purport to be a more balanced masking approach. They propose to train two encoders: one trained on streams with high-degree joints and low-motion keyframes masked out with greater frequency, and one trained on streams with low-degree joints and high-motion keyframes masked out more frequently. The encoders are otherwise trained in the say way as in prior work. The paper then proposes to combine the latents from the two encoders into one latent through a bi-directional attention mechanism. Finally, they propose to train a smaller (and cheaper!) encoder from the two combined encoders through a knowledge distillation approach.\n\nThe full proposed model (with two encoders) yields minor improvement over prior self-supervised learning methods (note, the accuracy of previous methods was already very high), and falls just short of supervised methods. The distilled model yields large efficiency improvements (a third the computational cost) with no significant loss in accuracy. Experiments on transfer between datasets suggest that the proposed method is better than baselines at transfer between different datasets.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims are clearly stated and the proposed modelling is motivated by a clear hypothesis. The evaluation is as extensive as prior work, covering three different datasets. A wide suite of baselines is considered. \n\nOne issue is that the accuracy differences both to prior methods and between ablations of the proposed model are very small. Without confidence intervals around the reported mean accuracies, it is not immediately clear if these differences are significant.", "audience_yn": "Yes", "audience_explanation": "Yes, for the sub-community of people working on action recognition this is certainly a relevant paper.", "requested_changes": "This is not a critical point to me, as considering the size of the datasets I imagine the differences in accuracy are already significant, but could you please provide statistical measures of uncertainty around the reported accuracies, or measures of statistical significance of the comparisons between the proposed method and baselines?", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "irAsy5VbQL", "reviewer": "Reviewer_KNA3", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes ASMa, a self-supervised framework for skeleton-based action recognition that introduces an asymmetric spatio-temporal masking strategy guided by joint degree and motion intensity. Two encoders with complementary masking (one masking high-degree/low-motion joints, the other low-degree/high-motion joints) are trained jointly using a Barlow Twins loss. A feature-alignment module fuses the representations via bi-directional cross-attention, and a knowledge-distillation step transfers the learned representation to a compact student model for edge deployment. The overall method collectively works well and achieves promising performance. Experiments on NTU RGB+D 60/120 and PKU-MMD show 4 – 7 % improvements over existing SSL baselines and near-supervised performance with > 90 % model compression.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims are well justified/supported by clear motivations or empirical evidence.", "audience_yn": "Yes", "audience_explanation": "The skeleton representation learning is a fundamental approach that may benefit several applications.", "requested_changes": "1. Ablation experiments of different encoders (f_theta and f_phi) using different augmentation strategies (different choices or combinations of spatial/temporal augmentations) are preferred, which could help further verify the superiority of the designed method. Table 3 and Figure 3 somehow provide these results, but are still not thorough enough. Adding more results regarding this and adding more discussions would be beneficial.\n\n2. Could the asymmetric masking probabilities be made learnable or adaptive during training? Are there any analyses/comparisons using different pre-defined thresholds/probabilities for masking?\n\n3. In knowledge distillation, have the authors compared the approach with feature-level distillation instead of logit-level KL loss?\n\n4. I am curious about how existing methods would perform when equipped with the proposed augmentation strategies.\n\n5. It would be helpful if the authors could include additional discussion to justify the advantages of skeleton-based representation learning compared with conventional image or video-based approaches, particularly in terms of efficiency, robustness, and motion understanding.\n\n6. Adding a concise pseudo-code or algorithmic outline would significantly improve the clarity of the overall pipeline and make the proposed method easier to follow.", "broader_impact_concerns": "n/a", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "6fTPRBIxBv", "reviewer": "Reviewer_RHAf", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposed an approach to self-supervised learning for skeleton based action recognition. It presents empirical analysis, and evaluations on NTU RGB+D 60, NTU RGB+D 120 and PKU-MMD datasets to show the improvements. The paper also presents detail ablation study. The paper is clear in general.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper uses standard benchmarks (NTU RGB+D 60/120, PKU-MMD). Details like ST-GCN backbone, Adam optimizer, 150 epochs, batch size 128, masking 9 joints/10 frames, and hyperparameters are specific and reproducible. \n\nNo apparent errors in formulations. The ablation study support the framework's design. \n\nThe citations are accurate and relevant.", "audience_yn": "Yes", "audience_explanation": "The paper is related to SSL, a core area in machine learning. Skeleton data involves graph-based learning. The asymmetric masking and feature alignment address generalization issues in spatio-temporal data. It is interested to those working on SSL, GNNs, and action recognition.", "requested_changes": "1. Include comparisons with more recent SSL methods and supervised backbones.\n2. Provide explicit discussion of the limitations, such as the sensitivity to skeleton noise, multi-person scenarios. \n3. Add a broader impacts section to address the privacy, bias mitigation, and dual-use risks. Suggest ethical guidelines and techniques for deployment.", "broader_impact_concerns": "The skeleton data derived from RGB+D videos can enable non-consensual tracking in public spaces. It may potentially violate privacy rights. (GDPR compliance is not discussed)\n\nThe technology could be misused for discriminatory surveillance. No discussion of misuse prevention.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)