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arXiv · 2412.10925

Video Representation Learning with Joint-Embedding Predictive Architectures

Katrina Drozdov·Ravid Shwartz-Ziv·Yann LeCun
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Abstract

Video representation learning is an increasingly important topic in machine learning research. We present Video JEPA with Variance-Covariance Regularization (VJ-VCR): a joint-embedding predictive architecture for self-supervised video representation learning that employs variance and covariance regularization to avoid representation collapse. We show that hidden representations from our VJ-VCR contain abstract, high-level information about the input data. Specifically, they outperform representations obtained from a generative baseline on downstream tasks that require understanding of the underlying dynamics of moving objects in the videos. Additionally, we explore different ways to incorporate latent variables into the VJ-VCR framework that capture information about uncertainty in the future in non-deterministic settings.

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Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun. 2024-12-14. Video Representation Learning with Joint-Embedding Predictive Architectures. https://arxiv.org/abs/2412.10925

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