Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework

Understand

We propose a self-supervised method to learn feature representations from videos.

• A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy.
• In such a case, different modalities of the same video are treated as positives and video clips from a different video are treated as negatives.
• Because the spatio-temporal information is important for video representation, we extend the negative samples by introducing intra-negative samples, which are transformed from the same anchor video by breaking temporal relations in video clips.