M ULTI - MODAL S ELF -S UPERVISION FROM G ENERALIZED D ATA T RANSFORMATIONS
Anonymous authors
Paper under double-blind review
A BSTRACT
In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations, such as image distortions.
In this paper, we show that, for videos, the answer is more complex, and that better results can be obtained by accounting for the interplay between invariance, distinctiveness, multiple modalities, and time.
We introduce Generalized Data Transformations (GDTs) as a way to capture this interplay.
GDTs reduce most previous selfsupervised approaches to a choice of data transformations, even when this was not the case in the original formulations.
They also allow to choose whether the representation should be invariant or distinctive w.r.t.
each effect and tell which combinations are valid, thus allowing us to explore the space of combinations systematically.
We show in this manner that being invariant to certain transformations and distinctive to others is critical to learning effective video representations, improving the state-of-the-art by a large margin, and even surpassing supervised pretraining.
We demonstrate results on a variety of downstream video and audio classiﬁcation and retrieval tasks, on datasets such as HMDB-51, UCF-101,
DCASE2014, ESC-50 and VGG-Sound.
In particular, we achieve new state-ofthe-art accuracies of 72.8% on HMDB-51 and 95.2% on UCF-101.
I NTRODUCTION
Recent works such as PIRL (Misra & van der Maaten, 2020), MoCo (He et al., 2019) and SimCLR (Tian et al., 2019) have shown that it is possible to pre-train state-of-the-art image representations without the use of any manually-provided labels.
Furthermore, many of these approaches use variants of noise contrastive learning (Gutmann & Hyvärinen, 2010).
Their idea is to learn a representation that is invariant to transformations that leave the meaning of an image unchanged (e.g. geometric distortion or cropping) and distinctive to changes that are likely to alter its meaning (e.g. replacing an image with another chosen at random).
An analysis of such works shows that a dominant factor for performance is the choice of the transformations applied to the data.
So far, authors have explored ad-hoc combinations of several transformations (e.g. random scale changes, crops, or contrast changes).
Videos further allow to leverage the time dimension and multiple modalities.
For example, Arandjelovic & Zisserman (2017);
Owens et al.
(2016) learn representations by matching visual and audio streams, as a proxy for objects that have a coherent appearance and sound.
Their formulation is similar to noise contrastive ones, but does not quite follow the pattern of expressing the loss in terms of data transformations.
Others (Chung & Zisserman, 2016; Korbar et al., 2018; Owens & Efros, 2018) depart further from standard contrastive schemes by learning representations that can tell whether visual and audio streams are in sync or not; the difference here is that the representation is encouraged to be distinctive rather than invariant to a time shift.
Overall, it seems that ﬁnding an optimal noise contrastive formulation for videos will require combining several transformations while accounting for time and multiple modalities, and understanding how invariance and distinctiveness should relate to the transformations.
However, the ad-hoc nature of these choices in previous contributions make a systematic exploration of this space rather difﬁcult.
In this paper, we propose a solution to this problem by introducing the Generalized Data Transformations (GDT; ﬁg. 1) framework.
GDTs reduce most previous methods, contrastive or not, to a noise contrastive formulation that is expressed in terms of data transformations only, making it simpler to systematically explore the space of possible combinations.
This is true in particular for multi-modal data, where separating different modalities can also be seen as a transformation of an input video.
The formalism also shows which combinations of different transformations are valid and how to enumerate them.
It also clariﬁes how invariance and distinctiveness to different effects can be incorporated in the formulation and when doing so leads to a valid learning objective.
These two aspects allows the search space of potentially optimal transformations to be signiﬁcantly constrained, making it amenable to grid-search or more sophisticated methods such as Bayesian optimisation.
By using GDTs, we make several ﬁndings.
First, we ﬁnd that using our framework, most previous pretext representation learning tasks can be formulated in a noise-contrastive manner, unifying previously distinct domains.
Second, we show that just learning representations that are invariant to more and more transformations is not optimal, at least when it comes to video data; instead, balancing invariance to certain factors with distinctiveness to others performs best.
Third, we ﬁnd that by investigating what to be variant to can lead to large gains in downstream performances, for both visual and audio tasks.
With this, we are able to set the new state of the art in audio-visual representation learning, with both small and large video pretraining datasets on a variety of visual and audio downstream tasks.
In particular, we achieve 95 .
2% and 72 .
8% on the standardized UCF-101 and HMDB-51 action recognition benchmarks.
R ELATED WORK
Self-supervised learning from images and videos.
A variety of pretext tasks have been proposed to learn representations from unlabelled images .
Some tasks leverage the spatial context in images (Doersch et al., 2015; Noroozi & Favaro, 2016) to train CNNs, while others create pseudo classiﬁcation labels via artiﬁcial rotations (Gidaris et al., 2018), or clustering features (Asano et al., 2020b; Caron et al., 2018; 2019; Gidaris et al., 2020; Ji et al., 2018).
Colorization (Zhang et al., 2016; 2017), inpainting (Pathak et al., 2016), solving jigsaw puzzles (Noroozi et al., 2017), as well as the contrastive methods detailed below, have been proposed for self-supervised image representation learning.
Some of the tasks that use the space dimension of images have been extended to the space-time dimensions of videos by crafting equivalent tasks.
These include jigsaw puzzles (Kim et al., 2019), and predicting rotations (Jing & Tian, 2018) or future frames (Han et al., 2019).
Other tasks leverage the temporal dimension of videos to learn representations by predicting shufﬂed frames (Misra et al., 2016), the direction of time (Wei et al., 2018), motion (Wang et al., 2019), clip and sequence order (Lee et al., 2017; Xu et al., 2019), and playback speed (Benaim et al., 2020;
Cho et al., 2020; Fernando et al., 2017).
These pretext-tasks can be framed as GDTs.
Multi-modal learning.
Videos, unlike images, are a rich source of a variety of modalities such as speech, audio, and optical ﬂow, and their correlation can be used as a supervisory signal.
This idea has been present as early as 1993 (de Sa, 1994).
Only recently, however, has multi-modal learning been used to successfully learn effective representations by leveraging the natural correspondence (Alwassel et al., 2020; Arandjelovic & Zisserman, 2017; Asano et al., 2020a; Aytar et al., 2016; Morgado et al., 2020;
Owens et al., 2016) and synchronization (Chung & Zisserman, 2016;
Korbar et al., 2018; Owens & Efros, 2018) between the audio and visual streams.
A number of recent papers have leveraged speech as a weak supervisory signal to train video representations (Li & Wang, 2020; Miech et al., 2020; Nagrani et al., 2020; Sun et al., 2019a;b).
Other works incorporate optical ﬂow and other modalities (Liu et al., 2019; Piergiovanni et al., 2020; Zhao et al., 2019) to learn representations. In (Tian et al., 2019), representations are learned with different views such as different color channels or modalities) to induce invariances.
In contrast, our work analyses multi-modal transformations and examines their utility when used as an invariant or variant learning signal.
Noise Contrastive Loss.
Noise contrastive losses (Gutmann & Hyvärinen, 2010; Hadsell et al., 2006) measure the similarity between sample pairs in a representational space and are at the core of several recent works on unsupervised feature learning.
It has been shown to yield good performance for learning image (Chen et al., 2020b;
He et al., 2019; Hénaff et al., 2019; Hjelm et al., 2019; Li et al., 2020; Misra & van der Maaten, 2020;
Oord et al., 2018; Tian et al., 2019; 2020; Wu et al., 2018) and video (Han et al., 2019; Li & Wang, 2020; Miech et al., 2020; Morgado et al., 2020;
Sohn, 2016; Sun et al., 2019a) representations, and circumvents the need to explicitly specify what information needs to be discarded via a designed task.
We leverage the noise contrastive loss as a learning framework to encourage the network to learn desired invariance and distinctiveness to data transformations.
The GDT framework can be used to combine and extend many of these cues, contrastive or not, in a single noise contrastive formulation.
M ETHOD
A data representation is a function f : X → R D mapping data points x to vectors f ( x ) .
Representations are useful because they help to solve tasks such as image classiﬁcation.
Based on the nature of the data and the task, we often know a priori some of the invariances that the representation should possess (for example, rotating an image usually does not change its class).
We can capture those by means of the contrast function 1 c ( x 1 , x 2 ) =
δ f ( x 1 )=
f
( x 2 ) , where c ( x 1 , x 2 ) =means that f is invariant to substituting x 2 for x 1 , while c ( x 1 , x 2 ) =
means that f is distinctive to this change.
Any partial knowledge of the contrast c can be used as a cue to learn f , but c is not arbitrary: in order for c to be valid , the expression c ( x 1 , x 2 ) = 1 must be an equivalence relation on X , i.e. be reﬂexive c ( x, x ) = 1 , symmetric c ( x 1 , x 2 )
=
c ( x 2 , x 1 ) and transitive c ( x 1 , x 2 ) =
c ( x 2 , x 3 )
= 1 ⇒ c
( x 1 , x 3 )
= 1 .
This is justiﬁed in Appendix A.1 and will be important in establishing which particular learning formulations are valid and which are not.
We introduce next our Generalized Data Transformations (GDTs) framework by generalizing two typical formulations: the ﬁrst is analogous to ‘standard’ methods such as MoCo (He et al., 2019) and SimCLR
(Chen et al., 2020b) and the second tackles multi-modal data.
Standard contrastive formulation.
Recall that the goal is to learn a function f
that is compatible with a known contrast c , in the sense explained above.
In order to learn f , we require positive ( c ( x 1 , x 2 ) = 1 ) and negative ( c ( x 1 , x 2 ) = 0 ) example pairs ( x 1 , x 2 ) .
We generate positive pairs by sampling x 1 from a data source and then by setting x 2 = g ( x 1 ) as a random transformation of the ﬁrst sample, where g ∈ G is called a data augmentation (e.g. image rotation).
We also generate negative pairs by sampling x 1 and x 2 independently.
It is convenient to express these concepts via transformations only.
To this end, let D =
( x 1 , . .
, x N ) ∈
X N be a collection of N i.i.d.
training data samples.
A Generalized Data Transformation (GDT) T : X N → Z is a mapping that acts on the set of training samples D to produce a new sample z = TD .
Note that the GDT is applied to the entire training set, so that sampling itself can be seen as a transformation.
In the simplest case, Z = X and a GDT T
= ( i, g ) extracts the sample corresponding to a certain index i and applies an augmentation g : X → X to it, i.e. TD =
g ( x i ) .
Usually, we want the function f to be distinctive to the choice of sample but invariant to its augmentation.
This is captured by setting the contrast c ( T, T (cid:48) )
2 to c (( i, g ) , ( i (cid:48) , g (cid:48) ))
=
δ i
=
i (cid:48) .
Given a batch T = { T 1 , . . .
, T K } of K GDTs, we then optimize a pairwise-weighted version of the noisecontrastive loss (Chen et al., 2020b;
Gutmann & Hyvärinen, 2010; Oord et al., 2018; Tian et al., 2019; Wu et al., 2018), the GDT-NCE loss:
[Equation]
Here, the scalar ρ is a temperature parameter and the weights w ( T, T (cid:48) ) are set to δ T (cid:54) =
T (cid:48) in order to discount contrasting identical transformations, which would result in a weak learning signal.
Minimizing eq.
(1) pulls together vectors f ( TD ) and f ( T (cid:48)
D )
if c ( T, T (cid:48) )
= 1 and pushes them apart if c ( T, T (cid:48) )
= 0 , similar to a margin loss, but with a better handling of hard negatives (Chen et al., 2020b; Khosla et al., 2020; Tian et al., 2019).
When using a single modality, T = T (cid:48) and positive pairs are computed from two differently augmented versions.
Multi-modal contrastive formulation.
We now further extend GDTs to handle multi-modal data.
In this case, several papers (Arandjelovic & Zisserman, 2017; Aytar et al., 2016; Korbar et al., 2018; Owens et al., 2016; Wei et al., 2018) have suggested to learn from the correlation between modalities, albeit usually not in a noise-contrastive manner.
In order to encode this with a GDT, we introduce modality projection transformations m ∈ M .
For example, a video x = ( v, a ) has a visual component v and an audio component a
and we we have two projections M = { m a , m v } extracting respectively the visual m v ( x ) = v and audio m a ( x ) = a signals.
We can plug this directly in eq.
(1) by considering GDTs T =
( i, m ) and setting TD = m ( x i ) , learning a representation f which is distinctive to the choice of input video, but invariant to the choice of modality.
General case.
Existing noise contrastive formulations learn representations that are invariant to an ad-hoc selection of transformations.
We show here how to use GDTs to build systematically new valid combinations of transformations while choosing whether to encode invariance or distinctiveness to each factor.
Together with the fact that all components, including data sampling and modality projection, are interpreted as transformations, this results in a powerful approach to explore a vast space of possible formulations systematically, especially for the case of video data with its several dimensions.
In order to do so, note that to write the contrastive loss eq.
(1), we only require: the contrast c ( T, T (cid:48) ) , the weight w ( T, T (cid:48) ) and a way of sampling the transformations T in the batch.
Assuming that each generalized transformation T =
t M ◦ · · · ◦ t 1 is a sequence of M transformations t m , we start by deﬁning the contrast c for individual factors as: [Equation] invariance
[Equation]
The overall contrast is then c ( T, T (cid:48) )
= (cid:81)
Mm =1 c ( t m , t (cid:48) m ) .
In this way, each contrast c ( t m , t (cid:48) m ) is an equivalence relation and so is c ( T, T (cid:48) )
(see Appendix A.1), making it valid in the sense discussed above.
We also assume that w ( T, T (cid:48) )
= 1 unless otherwise stated.
Next, we require a way of sampling transformations T in the batch.
Note that each batch must contain transformations that can be meaningfully contrasted, forming a mix of invariant and distinctive pairs, so they cannot be sampled independently at random.
Furthermore, based on the deﬁnition above, a single ‘distinctive’ factor in eq.
(2) such that t m (cid:54) =
t (cid:48)
m implies that c ( T, T (cid:48) )
= 0 .
Thus, the batch must contain several transformations that have equal distinctive factors in order to generate a useful learning signal.
A simple way to satisfy these constraints is to use a hierarchical sampling scheme (ﬁg. 1)
First, we sample K 1 instances of transformation t 1 ; then, for each sample t 1 , we sample K 2 instances of transformation t 2 and so on, obtaining a batch of K = (cid:81)
Mm =1 K m transformations T .
In this manner, the batch contains exactly K M × · · · × K m +1 transformations that share the same ﬁrst m factors ( t 1 = t (cid:48) 1 , . . .
t m = t (cid:48) m ).
While other schemes are possible, in Appendix A.2.1, we show that this is sufﬁcient to express a large variety of self-supervised learning cues that have been proposed in the literature.
In the rest of the manuscript, however, we focus on audio-visual data.
3. E XPLORING CONTRASTIVE AUDIO - VISUAL SELF - SUPERVISION
Within multi-modal settings, video representation learning on audio-visual data is particularly well suited for exploring the GDT framework.
Especially compared to still images, the space of transformations is much larger in videos due to the additional time dimension and modality.
It is therefore an ideal domain to explore how GDTs can be used to limit and explore the space of possible transformations and their quality as a learning signal when used as variances or invariances.
In order to apply our framework to audio-visual data, we start by specifying how transformations are sampled by using the hierarchical scheme introduced above (see also Figure 1).
We consider in particular GDTs of the type T =
( i, τ, m, g ) combining the following transformations.
The ﬁrst component i selects a video in the dataset.
We sample K
i (cid:29) 2 indices/videos and assume distinctiveness, so that c ( i, i (cid:48) )
=
δ i
=
i (cid:48) .
The second component τ contrasts different temporal shifts .
We sample K τ = 2 different values of a delay τ uniformly at random, extracting a 1s clip x iτ starting at time τ .
For this contrast, we will test the distinctiveness and invariance hypotheses.
The third component m contrasts modalities , projecting the video x iτ to either its visual or audio component m ( x iτ ) .
We assume invariance c ( m, m (cid:48) )
= 1 and always sample two such transformations m v and m a to extract both modalities, so K m = 2 .
The fourth and ﬁnal component g applies a spatial and aural augmentation TD =
g ( m ( x iτ )) , also normalizing the data.
We assume invariance c ( g, g (cid:48) )
=and pick K g = 1 .
The transformation g comprises a pair of augmentations ( g v , g a ) , where g v ( v ) extracts a ﬁxed-size tensor by resizing to a ﬁxed resolution a random spatial crop of the input video v , and g a ( a ) extracts a spectrogram representation of the audio signal followed by SpecAugment (Park et al., 2019) with frequency and time masking.
These choices lead to K
=
K
i K
τ K m K g
= 4 K
i transformations T in the batch T .
Testing invariance and distinctiveness hypotheses.
The transformations given above combine cues that were partly explored in prior work, contrastive and non-contrastive.
For example, Korbar et al.
(2018) (not noise-contrastive) learns to detect temporal shifts across modalities.
With our formulation, we can test whether distinctiveness or invariance to shifts is preferable, simply by setting c ( τ, τ (cid:48) )
= 1 or c ( τ, τ (cid:48) )
=
δ τ = τ (cid:48)
(this is illustrated in ﬁg. 1).
We can also set w ( τ, τ (cid:48) )
=
for τ (cid:54) =
τ (cid:48) to ignore comparisons that involve different temporal shifts.
We also test distinctiveness and invariance to time reversal (Wei et al., 2018), which has not previously been explored cross-modally, or contrastively.
This is given by a transformation r ∈ R = { r 0 , r 1 } , where r 0 is the identity and r 1 ﬂips the time dimension of its input tensor.
We chose these transformations, time reversal and time shift, because videos, unlike images, have a temporal dimension and we hypothesize that these signals are very discriminative for representation learning.
Ignoring comparisons.
Another degree of freedom is the choice of weighting function w ( T, T (cid:48) ) .
Empirically, we found that cross-modal supervision is a much stronger signal than within-modality supervision, so if T and T (cid:48) slice the same modality, we set w ( T, T (cid:48) )
=
(see Appendix for ablation).
Understanding combinations.
Finally, one may ask what is the effect of combining several different transformations in learning the representation f .
A ﬁrst answer is the rule given in eq.
(2) to combine individual contrasts c ( t m , t (cid:48) m ) in a consistent manner.
Because of this rule, to a ﬁrst approximation, f possesses the union of the invariances and distinctivenesses of the individual factors.
To obtain a more accurate answer, however, one should also account for the details of the batch sampling scheme and of the choice of weighing function w .
This can be done by consulting the diagrams given in ﬁg. 1 by: (1) choosing a pair of transformations T i and T j , (2) checking the value in the table (where 1 stands for invariance, 0 for distinctiveness and · for ignoring), and (3) looking up the composition of T i and T j in the tree to ﬁnd out the sub-transformations that differ between them as the source of invariance/distinctiveness.
E XPERIMENTS
We compare self-supervised methods on pretraining audio-visual representations.
Quality is assessed based on how well the pretrained representation transfers to other (supervised) downstream tasks.
We ﬁrst study the model in order to determine the best learning transformations and setup.
Then, we use the latter to train for longer and compare them to the state of the art.
Self-supervised pretraining.
For pretraining, we consider the standard audio-visual pretraining datasets, Kinetics-400 (Kay et al., 2017) and AudioSet (Gemmeke et al., 2017) [Table] and additionally, the recently released, VGG-Sound dataset (Chen et al., 2020a).
Finally, we also explore how our algorithm scales to even larger, less-curated datasets and train on IG65M (Ghadiyaram et al., 2019) as done in XDC (Alwassel et al., 2020).
GDT can leverage signals from both invariance and stronger variance transformation signals, that sole datasample
[Table]
misses [Table] Our method learns a pair of representations f = ( f v , f a ) for visual and audio information respectively and we refer to Appendix A.6 for architectural details.
Downstream tasks.
To assess the visual representation f v , we consider standard action recognition benchmark datasets, UCF-101 (Soomro et al., 2012) and HMDB51 (Kuehne et al., 2011b).
We test the performance of our pretrained models on the tasks of ﬁnetuning the pretrained representation, conducting few-shot learning and video action retrieval.
To assess the audio representation f a , we train a linear classiﬁer on frozen features for the common
ESC-50 (Piczak, 2015) and DCASE2014 (Stowell et al., 2015) benchmarks and ﬁnetune for VGG-Sound (Chen et al., 2020a).
The full details are given in the Appendix.
4. A NALYSIS OF GENERALIZED TRANSFORMATIONS
In this section, we conduct an extensive study on each parameter of the GDT transformation studied here, T
= ( i, τ, m, g ) , and evaluate the performance by ﬁnetuning our network on the UCF-101 and HMDB-51 action recognition benchmarks.
Sample distinctiveness and invariances.
First, we experiment with extending SimCLR to video data, as shown in Table 1(a)-(d).
This is an important base case as it is the standard approach followed by all recent self-supervised methods (Chen et al., 2020b; He et al., 2019; Wu et al., 2018).
For this, consider GDT of the type T =
( i, m, τ, g ) described above and set K i =
768 (the largest we can ﬁt in our setup), K m = 1 (only visual modality) and K g = 1 and only pick a single time shift
K τ = 1 .
We also set all transformation components to invariance ( c ( t m , t (cid:48) m )
= 1 ) except the ﬁrst that does sample selection.
Comparing row (a) to (b-d), we ﬁnd that adding invariances to time-shift (TS) and time-reversal (TR) consistently degrades the performance compared to the baseline in (a).
GDT variances and invariances Our framework allows ﬁne-grained and expressive control of which invariance and distinctiveness are learned.
To demonstrate this ﬂexibility, we ﬁrst experiment with having a single audio-visual (AV) invariance transformation, in this case data-sampling (DS), i.e. T
=
( i, τ, m, g ) .
We ﬁnd immediately an improvement in ﬁnetuning and retrieval performance compared to the SimCLR baselines, due to the added audio-visual invariance.
Second, we also ﬁnd that adding invariances to TR and TS does not yield consistent beneﬁts, showing that invariance to these transformations is not a useful signal for learning.
In rows (i-l), we explore the effect of being variant to two transformations, which is unique to our method.
We ﬁnd
that: (1) explicitly encoding variance improves representation performance for the TS and TR transformations ( 58 . 0 and 58 .
2 vs 56 . 9 ).
(2) Ignoring ( · ) the other transformation as
[Table] [Table]
opposed to forcefully being invariant to it works better (
[Table]
vs
[Table]
0 and
[Table]
0 vs 57 . 5 ). Finally, row (m), which shows the (DS, TR, TS)-variance case
[Table]
yields the best performance and improves upon the initial SimCLR baseline by more than 12% in accuracy and more than 15% in retrieval @ performance. Intuition While we only analyse a subset of possible transformations for video data, we nevertheless ﬁnd consistent signals: While both time-reversal and time-shift could function as a meaningful invariance transformation to provide the model with more difﬁcult positives a-priori , we ﬁnd that using them instead to force variances consistently works better.
One explanation for this might be that there is useful signal in being distinct to these transformations. E.g., for time-reversal, opening a door carries different semantics from from closing one, and for time-shift, the model might proﬁt from being able to differentiate between an athlete running vs an athlete landing in a sandpit, which could be both in the same video.
These ﬁndings are noteworthy, as they contradict results from the image self-supervised learning domain, where learning pretext-invariance can lead to more transferable representations (Misra & van der Maaten, 2020).
This is likely due to the fact that time shift and reversal are useful signals that both require learning strong video representations to pick up on.
If instead invariance is learned against these, the “free” information that we have from construction is discarded and performance degrades.
Instead, GDT allows one to leverage these strong signals for learning robust representations.
4. C OMPARISON TO THE STATE OF THE ART
Given our best learning setup from Sec. 4.1, we train for longer and compare our feature representations to the state of the art in common visual and aural downstream benchmarks.
Downstream visual benchmarks.
For video retrieval we report recall at 1, 5, 20 retrieved samples for split-1 of the HMDB-51 and
UCF-101 datasets in table 2 (the results for recall at 10 and 50 are provided in the Appendix).
Using our model trained on Kinetics-400, GDTsigniﬁcantly beats all other self-supervised methods by a margin of over 35% for both datasets.
For few-shot classiﬁcation , as shown in table 2, we signiﬁcantly beat the RotNet3D baseline on
UCF-101 by more than 10% on average for each shot with our Kinetics-400 pretrained model.
For video action recognition , we ﬁnetune our GDT pretrained network for UCF-101 and HMDBvideo classiﬁcation, and compare against state-of-the-art self-supervised methods in table 4.
When constrained to pretraining on the Kinetics datasets, we ﬁnd that our GDT pretrained model achieves very good results, similar to Morgado et al.
(2020) (developed concurrently to our own work).
When constrained to pretraining on the AudioSet (Gemmeke et al., 2017) dataset, we also ﬁnd state-ofthe-art performance among all self-supervised methods, particularly on HMDB-51.
We get similar performance to XDC on UCF-101.
Lastly, we show the scalability and ﬂexibility of our GDT framework by pretraining on the IG65M dataset (Ghadiyaram et al., 2019).
With this, our visual feature representation sets a new state of the art among all self-supervised methods, particularly by a margin of > 4% on the HMDB-51 dataset.
On UCF-101, we set similar state-of-the-art performance [Table] [Table]
[Table] with XDC. Along with XDC, we beat the Kinetics supervised pretraining baseline using the same architecture and ﬁnetuning protocol [Table]
[Table]
Downstream audio benchmarks.
For audio classiﬁcation we ﬁnd that we achieve state-of-theart performance among all self-supervised methods on both
DCASE2014 (DC) and ESC-50 (ESC), and also surpass supervised performance on VGG-Sound with 54 .
8% mAP and 97 .
AUC (see Tab. 5).
C ONCLUSION
We introduced the framework of Generalized Data Transformations (GDTs), which allows one to capture, in a single noise-contrastive objective, cues used in several prior contrastive and noncontrastive learning formulations, as well as easily incorporate new ones.
The framework shows how new meaningful combinations of transformations can be obtained, encoding valuable invariance and distinctiveness that we want our representations to learn.
Following this methodology, we achieved state-of-the-art results for self-supervised pretraining on standard downstream video action recognition benchmarks, even surpassing supervised pretraining.
Overall, our method signiﬁcantly increases the expressiveness of contrastive learning for self-supervision, making it a ﬂexible tool for many multi-modal settings, where a large pool of transformations exist and an optimal combination is sought.
A A PPENDIX A. T HEORY
Full knowledge of the contrast function c only speciﬁes the level sets of the representation f .
Lemma 1.
The contrast c ( x 1 , x 2 ) =
δ f ( x 1 )=
f ( x 2 ) deﬁnes f = ι ◦ ˆ f up to an injection ι : X /f → Y , where X /f is the quotient space and ˆ f : X → X /f is the projection on the quotient.
Proof.
This is a well known fact in elementary algebra.
Recall that the quotient X /f is just the collection of subsets X ⊂ X where f ( x ) is constant.
It is easy to see that this is a partition of X .
Hence, we can deﬁne the map ˆ f : X (cid:55)→ f ( x ) where x is any element of X (this is consistent since f ( x ) has, by deﬁnition, only one value over X ).
Furthermore, if ι : x (cid:55)→ X = { x ∈ X : f ( x (cid:48) )
=
f ( x ) } is the projection of x to its equivalence class X , we have f ( x ) =
ˆ f ( ι ( x )) .
Lemma 2.
c ( x 1 , x 2 ) = 1 is an equivalence relation if, and only if, there exists a function f such that c ( x 1 , x 2 ) =
δ f ( x 1 )=
f
( x 2 ) .
Proof.
If c ( x 1 , x 2 ) = 1 deﬁnes an equivalence relation on X , then such a function is given by the projection on the quotient ˆ f : X → X /c
=
Y .
On the other hand, setting c ( x 1 , x 2 )
=
δ f ( x 1 )=
f ( x 2 ) = 1 for any given function f is obviously reﬂexive, symmetric and transitive because the equality f ( x 1 ) =
f ( x 2 ) is.
The following lemma suggests that deﬁning a contrast c ( T, T (cid:48) ) on transformations instead of data samples is usually acceptable.
Lemma 3.
If c ( T, T (cid:48) )
= 1 deﬁnes an equivalence relation on GDTs, and if TD = TD (cid:48)
⇒ T = T (cid:48)
(i.e. different transformations output different samples), then setting c ( TD, T (cid:48) D ) =
c ( T, T (cid:48) ) deﬁnes part of an admissible sample contrast function.
Proof.
If x = TD , x (cid:48) =
T (cid:48)
D are obtained from some transformations T and T (cid:48) , then these must be unique by assumption.
Thus, setting c ( x, x (cid:48) )
=
c ( T, T (cid:48) ) is well posed.
Reﬂectivity, symmetry and transitivity are then inherited from the latter.
Lemma 4.
Let c ( t m , t (cid:48) m )
= 1 be reﬂexive, symmetric and transitive.
Their product c ( T, T (cid:48) ) =
(cid:81)
Mm =1 c ( t m , t (cid:48) m )
= has then the same properties.
Proof.
The reﬂexive and symmetric properties are obviously inherited.
For the transitive property, note that c ( T, T (cid:48) )
= 1 if, and only if, ∀ m : c ( t m , t (cid:48) m ) = 1 .
Then consider:
[Equation] [Equation]
A. G ENERALITY OF GDT
Here, we show that our GDT formulation can encapsulate and unify other self-supervised works in the literature.
We break it down it into two sections:
Mapping contrastive to GDT contrastive Recently, a number of papers have presented contrastive formulations for image representation learning such as, NPID (Wu et al., 2018), PIRL (Misra & van der Maaten, 2020), MoCo (He et al., 2019) and SimCLR (Chen et al., 2020b).
These methods are all essentially built on what we have introduced as the “data-sampling transformation”
T =
( i, g ) , that samples an image with index i and applies augmentation g .
For NPID, MoCo and
SimCLR, the main objective is to solely be distinctive to the image index, hence K
= K
i K g =
B (i.e. the batchsize B ) for NPID, due to the use of a memorybank and K
=
K
i K g = 2 B for
SimCLR and MoCo.
For PIRL, one additional transformation to be invariant to is added.
For example, in the case of rotation, the PIRL encodes sample-distinctiveness to the non-rotated inputs
K =
K i K g
= B in the memorybank, while the examples are used for constructing both invariance to the original inputs, as well as sample distinctiveness.
Non
-contrastive to GDT contrastive reduction.
In non-contrastive self-supervised formulations, one trains Φ( x ) =
y to regress y from x , where y is some “pretext” task label.
These labels can be obtained from the data, e.g. arrow of time (Wei et al., 2018)
, rotation (Gidaris et al., 2018; Jing &
Tian, 2018), shufﬂed frames (Misra et al., 2016), jigsaw conﬁgurations (Kim et al., 2019; Noroozi et al., 2017), or playback speed (Benaim et al., 2020; Cho et al., 2020).
We can reduce these pretext tasks to GDTs in two ways.
The ﬁrst ‘trivial’ reduction amounts to interpreting the supervision y as an additional pseudo-modality.
Consider for example RotNet; in this case, the label y should record the amount of rotation applied to the input image.
We can achieve this effect by starting from data z = ( x, 0) where x is an image and 0 a rotation angle.
We then sample transformation t r (rotation) and deﬁne its action as t r ( z ) =
( t r ( x ) , t r (0)) where t r (0) =
r is simply the rotation angle applied and t r ( x ) the rotated image.
We consider modality slicing transformations m x ( z ) = x and m r ( z )
= r .
To form a batch, we sample GDTs of the type T =
( i, t r , m ) , where i is sampled at random, for each i , t r is exhaustively sampled in a set of four rotations (0, 90, 180, 270 degrees) and, for each rotation t r , m is also exhaustively sampled, for a total of K
i K
r K m = 8 K i transformations in the batch.
We deﬁne c ( T, T (cid:48) )
=
c (( i, t r , m ) , ( i (cid:48) ,
[Equation]
r (cid:48) , m (cid:48) ))
=
[Equation]
r = r (cid:48) (note that we do not learn to distinguish different images; GDTs allow us to express this case naturally as well).
We deﬁne w ( T, T (cid:48) )
=
δ i
=
i (cid:48)
δ m (cid:54) = m (cid:48) so that images are treated independently in the loss and we always compare a pseudo modality (rotated image) with the other (label).
Finally, the network f r ( r ) =
e r ∈ { 0 , 1 } 4 operating on the label pseudo-modality trivially encodes the latter as a 1-hot vector.
Then we see that the noise-contrastive loss reduces to
[Equation]
which is nearly exactly the same as a softmax loss for predicting the rotation class applied to an image.
There are other reductions as well, which capture the spirit if not the letter of a training signal.
For instance, in RotNet, we may ask if two images are rotated by the same amount .
This is an interesting example as we do not wish to be distinctive to which image sample is taken, only to which rotation is applied.
This can also be captured as a GDT because the sampling process itself is a transformation.
In this case, the set of negatives will be the images rotated by a different amount, while the positive example will be an image rotated by the same amount.
Thus, pretext task-originating transformations that have not even been explored yet can be put into our framework and, as we show in this paper, be naturally combined with other transformations leading to even stronger representations.
A.2. P OTENTIAL APPLICATION TO TEXT - VIDEO LEARNING
While we focus on audio-visual representation learning due to the multitude of potentially interesting learning signals, it is also possible to apply our framework to other multi-modal settings, such as video-text. Instead of a ResNet-9 as audio encoder, a text-encoder such as wordembeddings Mikolov et al. ; Pennington et al.
(2014) with an MLP or a transformer (Vaswani et al., 2017) can be used for encoding the textual inputs and we can train with a cross-modal NCE loss as done currently for audio-visual representation learning in our GDT framework.
While the visual transformations can be kept as described in the paper, we can use transformations for text, such as sentence shufﬂing Wei & Zou (2019), or random word swaps Wei & Zou (2019).
Moreover, unlike prior works in the literature Li & Wang (2020); Miech et al.
(2019), which mostly focused on model and loss improvements for video-text learning, our framework would allow us to investigate whether it is more desirable to encode either invariance or disctinctiveness to these text transformations for effective video-text representation learning.
A. M ODALITY ABLATION
In Table A.1, we provide the results of running our baseline model (sample-distinctiveness only) within-modally instead of across modalities and ﬁnd a sharp drop in performance. [Table] A.
[Table]
D ATASET DETAILS
The Kinetics-400 dataset (Kay et al., 2017) is human action video dataset, consisting of 240k training videos, with each video representing one of 400 action classes.
After ﬁltering out videos without audio, we are left with 230k training videos, which we use for pretraining our model.
VGGSound (Chen et al., 2020a) is a recently released audio-visual dataset consisting of 200k short video clips of audio sounds, extracted from videos uploaded to YouTube.
We use the training split after ﬁltering out audio (170k) for pretraining our model.
Audioset (Gemmeke et al., 2017) is a large-scale audio-visual dataset of 2.1M videos spanningaudio event classes.
We use the training split (1.8M) for pretraining our model.
IG65M (Ghadiyaram et al., 2019) is a large-scale weakly supervised dataset collected from a social media website, consisting of 65M videos of human action events.
We use the all the videos in the dataset for pretraining.
HMDB-51 (Kuehne et al., 2011a) consists of 7K video clips spanning 51 different human activities.
HMDB-51 has three train/test splits of size 5k/2k respectively.
UCF-101 (Soomro et al., 2012) contains 13K videos from 101 human action classes, and has three train/test splits of size 11k/2k respectively.
ESC-50 (Piczak, 2015) is an environmental sound classiﬁcation dataset which has 2K sound clips of 50 different audio classes.
ESC-50 has 5 train/test splits of size 1.6k/400 respectively.
DCASE2014 (Stowell et al., 2015) is an acoustic scenes and event classiﬁcation dataset which has 100 training and 100 testing sound clips spanning 10 different audio classes.
A. P REPROCESSING DETAILS
The video inputs are 30 consecutive frames from a randomly chosen starting point in the video.
These frames are resized such that the shorter side is between 128 and 160, and a center crop of size 112 is extracted, with no color-jittering applied.
A random horizontal ﬂip is then applied with probability 0.5, and then the inputs’ channels are z-normalized using mean and standard deviation statistics calculated across each dataset.
One second of audio is processed as a 1 × 257 × 99 image, by taking the log-mel bank features with 257 ﬁlters and 199 time-frames after random volume jittering between 90% and 110% is applied to raw waveform, similar to (Arandjelovic & Zisserman, 2017).
The spectrogram is then Z-normalized, as in (Korbar et al., 2018).
Spec-Augment is then used to apply random frequency masking to the spectrogram with maximal blocking width 3 and sampled 1 times.
Similarly, time-masking is applied with maximum width 6 and sampled 1 times.
A. P RETRAINING DETAILS
We use R(2+1)D-18 (Tran et al., 2018) as the visual encoder f v and ResNet (He et al., 2016) with 9 layers as the audio encoder f
a unless otherwise noted; both encoders produce a ﬁxed-dimensional output ( 512 -D) after global spatio-temporal average pooling.
Both vectors are then passed through two fully-connected layers with intermediate size of 512 to produce 256 -D embeddings as in (Bachman et al., 2019) which are normalized by their L2-norm (Wu et al., 2018).
The embedding is used for computing the contrastive loss, while for downstream tasks, a linear layer after the global spatiotemporal average pooling is randomly intialized.
For NCE contrastive learning, the temperature ρ is set as 1 / 0 .
For optimizing these networks, we use SGD.
The SGD weight decay is 10 − 5 and the SGD momentum is 0 .
We use a mini-batch size of 12 on each of our 64 GPUs giving an effective batch size of 768 for distributed training.
The initial learning rate is set to 0 .
01 which we linearly scale with the number of GPUs, after following a gradual warm-up schedule for the ﬁrstepochs (Goyal et al., 2017).
For both Kinetics and VGG-Sound, we train for 200 epochs (3 days), while for Audioset and IG65M, we train for 50 epochs (5 days) and 2 epochs (7 days) respectively.
A. A BLATION EXPERIMENT DETAILS
For the ablations, we only train for 100 epochs on the Kinetics-400 dataset.
For both downstream tasks, we only evaluate on the ﬁrst fold each but found the performance between folds to be close (within 1-2%) [Table]
A. F ULL VIDEO ACTION RETRIEVAL TABLE
In Table A.2 we show the full table on video action retrieval and compare to several of our models, pretrained on different datasets.
[Table]
A. F ULL VIDEO ACTION RECOGNITION TABLE
[Table]
A. E VALUATION DETAILS
All evaluation code is provided in the Supplementary Material.
Video During training, we take 10 random clips of length 32 frames from each video.
For video clip augmentations, we follow a standard protocol as in (Korbar et al., 2018).
During evaluation, we uniformly sample 10 clips from each video, average softmax scores, and predict the class having the highest mean softmax score.
We then measure the mean video top-1 accuracy across all videos and all ofﬁcial folds.
During training, we use SGD with initial learning rate 0 .
0025 , which we gradually warm up to 2 · 10 − 2 in the ﬁrst 2 epochs.
The weight decay is set to 5 · 10 − 3 and momentum to 0 .
We use a mini-batch size of 32 and train for 12 epochs with the learning rate multiplied by 5 · 10 −at 6 and 10 epochs.
We compare our GDT pretrained model with both self-supervised methods, and supervised pretraining, and report average top-1 accuracies on UCF101 and HMDB-51 action recognition task across three folds in table A.3.
Few-shot classiﬁcation We follow the protocol in (Jing & Tian, 2018) and evaluate our our
GDT pretrained network using few-shot classiﬁcation on the UCF-101 dataset, and additionally on HMDB-51.
We randomly sample n videos per class from the train set, average the encoder’s global average pooling features from ten clips per training sample and measure classiﬁcation accuracy performance on the validation set using a k -nearest neighbor classiﬁer, with k set to 1 .
Retrieval We follow the standard protocol as outlined in (Xu et al., 2019).
We use the split 1 of
UCF101, and additionally HMDB-51.
We uniformly sample 10 clips per video, and average the max-pooled features after the last residual block for each clip per video.
We use these averaged features from the validation set to query the videos in the training set.
The cosine distance of representations between the query clip and all clips in the training set are computed.
When the class of a test clip appears in the classes of k nearest training clips, it is considered to be correctly predicted.
We report accuracies for k = 1 , 5 , 10 , 20 , 50 and compare with other self-supervised methods on UCF101 and HMDB-51 in table A.2.
Audio We extract 10 equally spaced 2-second sub-clips from each full audio sample of ESC50 (Piczak, 2015) and 60 1-second sub-clips from each full sample of DCASE2014 (Stowell et al., 2015).
We save the activations that result from the audio encoder to quickly train the linear classiﬁers.
We use activations after the last convolutional layer of the ResNet-9 and apply a max pooling with kernelsize (1,3) and stride of (1,2) without padding to the output.
For both datasets, we then optimize a L2 regularized linear layer with batch size 512 using the Adam optimizer (Kingma & Ba, 2015) with learning rate 1 · 10 − 4 , weight-decay set to 5 · 10 − 4 and the default parameters.
The classiﬁcation score for each audio sample is computed by averaging the sub-clip scores in the sample, and then predicting the class with the highest score.
The mean top-1 accuracy is then taken across all audio clips and averaged across all ofﬁcial folds.
For VGG-Sound (Chen et al., 2020a), we follow their evaluation metrics but follow a much shorter training schedule as our model is pretrained.
We optimize the network with batch size 128 using the Adam optimizer (Kingma & Ba, 2015) with learning rate 1 · 10 − 4 for the pretrained backbone and 1 · 10 − 3 for the newly randomly initialized linear layer, weight-decay set to 1 · 10 − 5 and the default parameters.
We drop the learning rate at 10 and 20 epochs and train for 30 epochs, which takes less than 10h on a single Nvidia GTX
Titan GPU.