TriBERT: Human-centric Audio-visual Representation Learning

The recent success of transformer models in language , such as BERT , has motivated the use of such architectures for multi-modal feature learning and tasks . However , most multi-modal variants ( e.g. , ViLBERT ) have limited themselves to visuallinguistic data . Relatively few have explored its use in audio-visual modalities , and none , to our knowledge , illustrate them in the context of granular audio-visual detection or segmentation tasks such as sound source separation and localization . In this work , we introduce TriBERT – a transformer-based architecture , inspired by ViLBERT , which enables contextual feature learning across three modalities : vision , pose , and audio , with the use of flexible co-attention . The use of pose keypoints is inspired by recent works that illustrate that such representations can significantly boost performance in many audio-visual scenarios where often one or more persons are responsible for the sound explicitly ( e.g. , talking ) or implicitly ( e.g. , sound produced as a function of human manipulating an object ) . From a technical perspective , as part of the TriBERT architecture , we introduce a learned visual tokenization scheme based on spatial attention and leverage weak-supervision to allow granular cross-modal interactions for visual and pose modalities . Further , we supplement learning with sound-source separation loss formulated across all three streams . We pre-train our model on the large MUSIC21 dataset and demonstrate improved performance in audio-visual sound source separation on that dataset as well as other datasets through fine-tuning . In addition , we show that the learned TriBERT representations are generic and significantly improve performance on other audio-visual tasks such as cross-modal audio-visual-pose retrieval by as much as 66.7 % in top-1 accuracy . 1 Introduction . Multi-modal audio-visual learning [ 57 ] , which explores and leverages the relationship between visual and auditory modalities , has started to emerge as an important sub-field of machine learning and computer vision . Examples of typical tasks include : audio-visual separation and localization , where the goal is to segment sounds produced by individual objects in an audio and/or to localize those objects in a visual scene [ 15 , 16 , 42 , 55 ] ; and audio-visual correspondence , where the goal is often audio-visual retrieval [ 23 , 47 , 53 ] . Notably , some of the most recent audio-visual methods [ 15 ] leverage human pose keypoints , or landmarks , as an intermediate or contextual representation . This tends to improve the overall performance of sound separation , as pose and motion are important cues for characterising both the type of instrument being played and , potentially , over time , the rhythm of the individual piece [ 15 ] . It can also serve as an intermediate representation when generating video from acoustic signals [ 8 , 44 ] for example . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Most of the existing architectures tend to extract features from the necessary modalities using pre-trained backbones ( e.g. , CNNs applied to video frames [ 55 ] , object regions [ 16 ] , and audio spectrograms ; and/or graph CNN for human pose [ 15 ] ) and then construct problem-specific architectures that often utilize simple late fusion for cross-modal integration in decoding ( e.g. , to produce spectrogram masks [ 15 , 16 , 55 ] ) . This is contrary to current trends in other multi-modal problem domains , where over the past few years , approaches have largely consolidated around generic multi-modal feature learning architectures that are task agnostic to produce contextualized feature representations and then fine-tune those representations to a variety of tasks ( e.g. , visual question answering ( VQA ) or reasoning ( VCR ) ) and datasets . Examples of such architectures include ViLBERT [ 33 ] , VL-BERT [ 46 ] , and Unicoder-VL [ 31 ] , all designed specifically for visual-linguistic tasks . Audio-visual representation learning has , in comparison , received much less attention . Most prior works [ 51 ] assume a single sound source per video and rely on audio-visual alignment objectives . Exceptions include [ 39 ] , which relies on proposal mechanisms and multiple-instance learning [ 49 ] or co-clustering [ 25 ] . These approaches tend to integrate multi-modal features extracted using pre-trained feature extractors ( e.g. , CNNs ) at a somewhat shallow level . The very recent variants [ 6 , 28 , 35 ] leverage transformers for audio-visual representation learning through simple classification [ 6 ] and self-supervised [ 28 ] or contrastive [ 35 ] learning objectives while only illustrating performance on video-level audio-visual action classification . To the best of our knowledge , no audio-visual representation learning approach to date has explored pose as one of the constituent modalities ; nor has shown that feature integration and contextualization at a hierarchy of levels , as is the case for BERT-like architectures , can lead to improvements on granular audio-visual tasks such as audio-visual sound source separation . To address the aforementioned limitations , we formulate a human-centric audio-visual representation learning architecture , inspired by ViLBERT [ 33 ] and other transformer-based designs , with an explicit goal of improving the state-of-the-art in audio-visual sound source separation . Our transformer model takes three streams of information : video , audio , and ( pose ) keypoints and co-attends among those three modalities to arrive at enriched representations that can then be used for the final audiovisual sound separation task . We illustrate that these representations are general and also improve performance on other auxiliary tasks ( e.g. , forms of cross-modal audio-visual-pose retrieval ) . From a technical perspective , unlike ViLBERT and others , our model does not rely on global frame-wise features nor an external proposal mechanism . Instead , we leverage a learned attention to form visual tokens , akin to [ 42 ] , and leverage weakly supervised objectives that avoid single sound-source assumptions for learning . In addition , we introduce spectrogram mask prediction as one of our pre-training tasks to enable the network to better learn task-specific contextualized features . Contributions : Foremost , we introduce a tri-modal VilBERT-inspired model , which we call TriBERT , that co-attends among visual , pose keypoint , and audio modalities to produce highly contextualized representations . We show that these representations , obtained by optimizing the model with respect to uni-modal ( weakly-supervised ) classification and sound separation pretraining objectives , produce features that improve audio-visual sound source separation at large and also work well on other downstream tasks . Further , to avoid reliance on the image proposal mechanisms , we formulate tokenization in the image stream in terms of learned attentional pooling , which is learned jointly . This alleviates the need for externally trained detection mechanisms , such as Faster R-CNN and variants . We illustrate competitive performance on a number of granular audio-visual tasks both by using the TriBERT model directly , using it as a feature extractor , or by fine-tuning it . 2 Related works . Audio-visual Tasks . There exists a close relationship between visual scenes and the sounds that they produce . This relationship has been leveraged to complete various audio-visual tasks . Based on [ 57 ] ’ s survey of audio-visual deep learning , these tasks can be categorized into four subfields , three of which are addressed in this paper and described in the following three subsections . Audio-visual Sound Source Separation and Localization . Sound source separation and the related task of sound source localization have been studied quite extensively . Previous works studying separation , also known as the cocktail party problem [ 19 ] , leverage multi-modal audio-visual information [ 11 , 14 ] to help improve performance with respect to their audio-only counterparts [ 26 , 34 ] . Examples include learning correlations between optical flow and masked frequencies [ 9 , 13 ] , using graphical models [ 21 ] , detecting salient motion signals that correspond to audio events [ 30 , 40 ] , and extracting pose keypoints to model human movements [ 15 ] . A close connection between separation and localization has also been illustrated [ 40 , 43 , 55 , 56 ] . For example , [ 16 , 42 ] both formulate the task as one of auditory and visual co-segmentation , either with pre-trained object regions obtained by the detector [ 16 ] or directly from the image [ 42 ] . All of these approaches contain highly specialized architectures with custom fusion schemes . We aim to leverage the flexibility of transformer models to create generalized multi-modal representations that improve on audio-visual tasks . Audio-visual Representation Learning . The goal is typically to learn aligned representations . The quality of these representations has been shown to greatly impact the overall performance of tasks downstream [ 4 ] . A common strategy for representation learning is to introduce a proxy task . In the audio-visual space , past works [ 1 , 2 , 38 ] have trained networks by having them watch and listen to a large amount of unlabeled videos containing both positive samples of matching audio and visual pairs and negative samples of mismatched pairs ; the proxy task is binary classification of whether or not the audio and visual match each other . Other proxy tasks include determining whether or not an audio-visual pair is time synchronized [ 27 ] ; and [ 29 ] uses a classification task to identify the correct visual clip or audio stream from a set with negative samples . However , these works rely on the assumption that only one main sound source occurs at a time and everything else is background noise . Our model uses a weakly supervised proxy objective to learn representations for multiple sources of sound ( two in experiments ) occurring simultaneously and also learns to incorporate pose features . Audio-visual Correspondence Learning . One of the fundamental tasks in correspondence learning related to our work is cross-modality retrieval . Most prior works focus on audio-visual retrieval [ 24 , 36 , 48 ] and propose learning a joint embedding space where both modalities can be mapped to . In this space , semantically related embeddings are close to each other and thus retrieval can be performed by selecting the closest embedding to the query from the alternate modality . In our work , we demonstrate that enhanced feature representations obtained by our pretrained model capture aligned semantics and lead to much better cross-modal retrieval than baseline representations . Visiolinguistic Representation Learning . Our model is inspired by the recent successes of visiolinguistic representations . Most such approaches leverage a combination of uni-modal and cross-modal transformer modules to pre-train generic visiolinguistic representations on masked language and/or multi-modal alignment tasks . For example , [ 33 ] proposes separate streams for each modality that communicate with each other through co-attention , while [ 46 ] uses a single-stream model that takes both visual and linguistic embeddings as input . In our work , we also leverage co-attention modules to learn joint representations between audio , pose , and vision modalities . However , in addition to extending co-attention , we also focus on reformulating image tokenization and demonstrate the ability to learn with weakly-supervised classification objectives as opposed to masked token predictions . 3 Approach . We introduce TriBERT , a network that learns a joint representation of three modalities : vision , pose , and audio . We briefly review ViLBERT , the architecture that inspired TriBERT , in Section 3.1 . We then describe our TriBERT architecture in Section 3.2 , including pretraining tasks and objectives . 3.1 Reviewing Vision-and-Language BERT ( ViLBERT ) . Motivated by the recent success of the BERT architecture for transfer learning in language modeling , Lu et al . [ 33 ] proposed ViLBERT to represent text and visual content jointly . ViLBERT is a twostream model for image regions and text segments . Each stream is similar to the BERT architecture , containing a series of transformer blocks ( TRM ) [ 50 ] . Given an image I with corresponding regions-of-interest ( RoIs ) or bounding boxes v0 , v1 , ... vN and an input sentence S with word tokens w0 , w1 , ... wT , the final output representations are hv0 , hv1 , ... , hvN and hw0 , hw1 , ... , hwT for the visual and linguistic features , respectively . To exchange information between the two modalities , the authors introduced a co-attentional transformer layer which computes query ( Q ) , key ( K ) , and value ( V ) pairs like a standard transformer block . The keys and values from each modality are then fed to the multi-headed attention block of the other modality . The attention block in each stream generates attention-pooled features conditioned on the other modality and outputs a multi-modal joint representation which outperforms single-stream models across multiple vision-and-language tasks .

Summary: This work proposes TriBERT to handle the modalities of RGB image, poses and audios for tackling the task of sound source separation. The BERT model is used to extract features following ViLBERT. Experiments show appealing results on sound separation metrics compared with the author-reproduced previous SOTA methods.