Summary of Video Representation Learning with Joint-embedding Predictive Architectures, by Katrina Drozdov et al.
Video Representation Learning with Joint-Embedding Predictive Architectures
by Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Video representation learning is a crucial area of research in machine learning, with applications in various domains. Our paper introduces Video JEPA with Variance-Covariance Regularization (VJ-VCR), a joint-embedding predictive architecture that leverages variance and covariance regularization to prevent representation collapse. We demonstrate that the hidden representations generated by VJ-VCR contain abstract, high-level information about the input data, surpassing those obtained from generative baselines in downstream tasks that require understanding of object dynamics. Furthermore, we investigate various ways to incorporate latent variables into the VJ-VCR framework, which can capture uncertainty in non-deterministic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video representation learning is important for machines to understand videos better. Our paper presents a new way called Video JEPA with Variance-Covariance Regularization (VJ-VCR) that helps machines learn about videos without needing labels. We show that VJ-VCR creates hidden representations that are really good at understanding what’s happening in the video, even when objects are moving. This can help us better understand and make decisions based on videos. |
Keywords
» Artificial intelligence » Embedding » Machine learning » Regularization » Representation learning
