Self-supervised Contrastive Learning for Audio-Visual Action Recognition

• URL: http://arxiv.org/abs/2204.13386v2
• Date: Mon, 20 Mar 2023 07:33:09 GMT
• Title: Self-supervised Contrastive Learning for Audio-Visual Action Recognition
• Authors: Yang Liu, Ying Tan, Haoyuan Lan
• Abstract summary: The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. We propose an end-to-end self-supervised framework named Audio-Visual Contrastive Learning (A), to learn discriminative audio-visual representations for action recognition.
• Score: 7.188231323934023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. In this paper, we propose an end-to-end self-supervised framework named Audio-Visual Contrastive Learning (AVCL), to learn discriminative audio-visual representations for action recognition. Specifically, we design an attention based multi-modal fusion module (AMFM) to fuse audio and visual modalities. To align heterogeneous audio-visual modalities, we construct a novel co-correlation guided representation alignment module (CGRA). To learn supervised information from unlabeled videos, we propose a novel self-supervised contrastive learning module (SelfCL). Furthermore, we build a new audio-visual action recognition dataset named Kinetics-Sounds100. Experimental results on Kinetics-Sounds32 and Kinetics-Sounds100 datasets demonstrate the superiority of our AVCL over the state-of-the-art methods on large-scale action recognition benchmark.

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