Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations

Contrastive self-supervised learning has delivered impressive results in many audio-visual recognition tasks. However, existing approaches optimize for learning either global representations useful for high-level understanding tasks such as classification, or local representations useful for tasks such as audio-visual source localization and separation. While they produce satisfactory results in their intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for. In this work, we propose a versatile self-supervised approach to learn audio-visual representations that can generalize to both the tasks which require global semantic information (e.g. classification) and the tasks that require fine-grained spatio-temporal information (e.g. localization). We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn discriminative global-local visual information given the corresponding audio information. To show that our approach learns generalizable video representations, we evaluate it on various downstream scenarios including action/sound classification, lip reading, deepfake detection, and sound source localization.