Summary of An Image Is Worth More Than 16×16 Patches: Exploring Transformers on Individual Pixels, by Duy-kien Nguyen et al.
An Image is Worth More Than 16×16 Patches: Exploring Transformers on Individual Pixels
by Duy-Kien Nguyen, Mahmoud Assran, Unnat Jain, Martin R. Oswald, Cees G. M. Snoek, Xinlei Chen
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A research paper questions the necessity of inductive bias in modern computer vision architectures by showing that vanilla Transformers can achieve high performance by treating individual pixels as tokens. This is different from popular designs like Vision Transformer, which maintain locality in patches. The effectiveness of this approach is demonstrated across three tasks: supervised learning, self-supervised learning, and image generation. Although processing individual pixels may be less practical, the discovery has implications for designing next-generation architectures. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary A surprising finding challenges the idea that computer vision models need to focus on local neighborhoods. Instead, vanilla Transformers can work well by treating each pixel as a token. This is different from how Vision Transformer works. The paper shows that this approach does well in three tasks: classifying and predicting, generating images, and learning from partial data. While it may not be practical to process individual pixels, the discovery matters for building future models. |
Keywords
» Artificial intelligence » Image generation » Self supervised » Supervised » Token » Vision transformer