Advancing video self-supervised learning via image foundation models

Jingwei Wu

doi:10.1016/j.patrec.2025.03.015

Advancing video self-supervised learning via image foundation models

2025-06-01

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AI

DOI：10.1016/j.patrec.2025.03.015

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In the past decade, image foundation models (IFMs) have achieved unprecedented progress. However, the potential of directly using IFMs for video self-supervised representation learning has largely been overlooked. In this study, we propose an advancing video self-supervised learning (AdViSe) approach, aimed at significantly reducing the training overhead of video representation models using pre-trained IFMs. Specifically, we first introduce temporal modeling modules (ResNet3D) to IFMs, constructing a video representation model. We then employ a video self-supervised learning approach, playback rate perception, to train temporal modules while freezing the IFM components. Experiments on UCF101 demonstrate that AdViSe achieves performance comparable to state-of-the-art methods while reducing training time by 3.4x and GPU memory usage by 8.2x. This study offers fresh insights into low-cost video self-supervised learning based on pre-trained IFMs. Code is available at https://github.com/JingwWu/advise-video-ssl.

Keyword:

Video self-supervised learning

Video representation model

Image foundation model

Advancing video self-supervised learning via image foundation models

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