Video Representation Learning with Joint-Embedding Predictive Architectures

• URL: http://arxiv.org/abs/2412.10925v1
• Date: Sat, 14 Dec 2024 18:33:29 GMT
• Title: Video Representation Learning with Joint-Embedding Predictive Architectures
• Authors: Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun,
• Abstract summary: We present Video JEPA with Variance-Covariance Regularization (VJ-VCR): a joint-embedding predictive architecture for self-supervised video representation learning. We show that hidden representations from our VJ-VCR contain abstract, high-level information about the input data.
• Score: 23.250749688875196
• License:
• 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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