  Contrastive learning has delivered impressive results in many audio-visual
representation learning scenarios. However, existing approaches optimize for
learning either \textit{global} representations useful for tasks such as
classification, or \textit{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 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 audio signals. 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.
