Contrastive Learning of Global-Local Video Representations

Understand

Contrastive learning has delivered impressive results for various tasks in the self-supervised regime.

• However, existing approaches optimize for learning representations specific to downstream scenarios, i.e., \textit{global} representations suitable for tasks such as classification or \textit{local} representations for tasks such as detection and localization.
• While they produce satisfactory results in the intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for.
• In this work, we propose to learn video representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require local fine-grained spatio-temporal information (e.g., localization).