Audio-Visual Instance Discrimination with Cross-Modal Agreement

We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice versa. We show that optimizing for cross-modal discrimination, rather than within-modal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves state-of-the-art results when finetuned on action recognition tasks. While recent work in contrastive learning defines positive and negative samples as individual instances, we generalize this definition by exploring cross-modal agreement. We group together multiple instances as positives by measuring their similarity in both the video and the audio feature spaces. Cross-modal agreement creates better positive and negative sets, and allows us to calibrate visual similarities by seeking within-modal discrimination of positive instances.