Inter-intra Variant Dual Representations forSelf-supervised Video Recognition

TL;DR

This paper introduces a dual representation learning approach for self-supervised video recognition that captures both intra- and inter-variance, leading to significant performance improvements across multiple benchmarks.

Contribution

It proposes a novel dual representation method that encodes intra-variance with a shuffle-rank task and inter-variance with a contrastive loss, enhancing self-supervised video learning.

Findings

Achieves 82.0% accuracy on UCF101 with SimCLR

Attains 51.2% accuracy on HMDB51

Reaches 46.1% video retrieval accuracy on UCF101

Abstract

Contrastive learning applied to self-supervised representation learning has seen a resurgence in deep models. In this paper, we find that existing contrastive learning based solutions for self-supervised video recognition focus on inter-variance encoding but ignore the intra-variance existing in clips within the same video. We thus propose to learn dual representations for each clip which (\romannumeral 1) encode intra-variance through a shuffle-rank pretext task; (\romannumeral 2) encode inter-variance through a temporal coherent contrastive loss. Experiment results show that our method plays an essential role in balancing inter and intra variances and brings consistent performance gains on multiple backbones and contrastive learning frameworks. Integrated with SimCLR and pretrained on Kinetics-400, our method achieves $\textbf{82.0\%}$ and $\textbf{51.2\%}$ downstream classification accuracy on UCF101 and HMDB51 test sets respectively and $\textbf{46.1\%}$ video retrieval accuracy on UCF101, outperforming both pretext-task based and contrastive learning based counterparts. Our code is available at \href{https://github.com/lzhangbj/DualVar}{https://github.com/lzhangbj/DualVar}.