Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
This work addresses the challenge of representation learning in audio-visual data for researchers in multimodal AI, though it appears incremental as it builds on existing contrastive learning techniques.
The paper tackles the problem of improving unsupervised audio-visual pre-training by proposing a speed co-augmentation method that randomly changes playback speeds for both audio and video, resulting in significant enhancements in learned representations compared to vanilla contrastive learning.
This work aims to improve unsupervised audio-visual pre-training. Inspired by the efficacy of data augmentation in visual contrastive learning, we propose a novel speed co-augmentation method that randomly changes the playback speeds of both audio and video data. Despite its simplicity, the speed co-augmentation method possesses two compelling attributes: (1) it increases the diversity of audio-visual pairs and doubles the size of negative pairs, resulting in a significant enhancement in the learned representations, and (2) it changes the strict correlation between audio-visual pairs but introduces a partial relationship between the augmented pairs, which is modeled by our proposed SoftInfoNCE loss to further boost the performance. Experimental results show that the proposed method significantly improves the learned representations when compared to vanilla audio-visual contrastive learning.