Video Representation Learning with Joint-Embedding Predictive Architectures
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909428393967616 |
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| author | Drozdov, Katrina Shwartz-Ziv, Ravid LeCun, Yann |
| author_facet | Drozdov, Katrina Shwartz-Ziv, Ravid LeCun, Yann |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_10925 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Video Representation Learning with Joint-Embedding Predictive Architectures Drozdov, Katrina Shwartz-Ziv, Ravid LeCun, Yann Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Video Representation Learning with Joint-Embedding Predictive Architectures |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.10925 |