Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics

TL;DR

This paper introduces a self-supervised learning method for videos that uses spatio-temporal statistical summaries as a pretext task, improving performance on various video analysis benchmarks.

Contribution

It proposes a novel pretext task based on statistical summaries and spatial partitioning, enhancing self-supervised video representation learning.

Findings

Outperforms existing methods on multiple downstream tasks

Effective across various 3D backbone networks

Improves action recognition, retrieval, and scene understanding

Abstract

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. Our approach is inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents. To validate the effectiveness of the proposed approach, we conduct extensive experiments with four 3D backbone networks, i.e., C3D, 3D-ResNet, R(2+1)D and S3D-G. The results show that our approach outperforms the existing approaches across these backbone networks on four downstream video analysis tasks including action recognition, video retrieval, dynamic scene recognition, and action similarity labeling. The source code is publicly available at: https://github.com/laura-wang/video_repres_sts.