Cross-modal Self-Supervised Learning for Lip Reading
The goal of this work is to learn discriminative visual representations for lip reading without access to manual text annotation. Recent advances in cross-modal self-supervised learning have shown that the corresponding audio can serve as a supervisory signal to learn effective visual representations for lip reading. However, existing methods only exploit the natural synchronization of the video and the corresponding audio. We find that both video and audio are actually composed of speech-related information, identity-related information, and modal information. To make the visual representations (i) more discriminative for lip reading and (ii) indiscriminate with respect to the identities and modals, we propose a novel self-supervised learning framework called Adversarial Dual-Contrast Self-Supervised Learning (ADC-SSL), to go beyond previous methods by explicitly forcing the visual representations disentangled from speech-unrelated information. Experimental results clearly show that the proposed method outperforms state-of-the-art cross-modal self-supervised baselines by a large margin. Besides, ADC-SSL can outperform its supervised counterpart without any finetune.