A Large-Scale Analysis on Self-Supervised Video Representation Learning

Understand

Self-supervised learning is an effective way for label-free model pre-training, especially in the video domain where labeling is expensive.

• Existing self-supervised works in the video domain use varying experimental setups to demonstrate their effectiveness and comparison across approaches becomes challenging with no standard benchmark.
• In this work, we first provide a benchmark that enables a comparison of existing approaches on the same ground.
• Next, we study five different aspects of self-supervised learning important for videos; 1) dataset size, 2) complexity, 3) data distribution, 4) data noise, and, 5)feature analysis.