Self-Supervised Video Representation Learning With Odd-One-Out Networks

Understand

We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called "odd-one-out learning".

• In this task, the machine is asked to identify the unrelated or odd element from a set of otherwise related elements.
• We apply this technique to self-supervised video representation learning where we sample subsequences from videos and ask the network to learn to predict the odd video subsequence.
• The odd video subsequence is sampled such that it has wrong temporal order of frames while the even ones have the correct temporal order.