Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Temporal Synchronicity

• URL: http://arxiv.org/abs/2111.05329v1
• Date: Tue, 9 Nov 2021 20:24:19 GMT
• Title: Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Temporal Synchronicity
• Authors: Pritam Sarkar and Ali Etemad
• Abstract summary: CrissCross is a self-supervised framework for learning audio-visual representations. We show that by relaxing the temporal synchronicity between the audio and visual modalities, the network learns strong time-invariant representations.
• Score: 12.995632804090198
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present CrissCross, a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework whereby in addition to learning the intra-modal and standard 'synchronous' cross-modal relations, CrissCross also learns 'asynchronous' cross-modal relationships. We show that by relaxing the temporal synchronicity between the audio and visual modalities, the network learns strong time-invariant representations. Our experiments show that strong augmentations for both audio and visual modalities with relaxation of cross-modal temporal synchronicity optimize performance. To pretrain our proposed framework, we use 3 different datasets with varying sizes, Kinetics-Sound, Kinetics-400, and AudioSet. The learned representations are evaluated on a number of downstream tasks namely action recognition, sound classification, and retrieval. CrissCross shows state-of-the-art performances on action recognition (UCF101 and HMDB51) and sound classification (ESC50). The codes and pretrained models will be made publicly available.

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