Summary of A Spitting Image: Modular Superpixel Tokenization in Vision Transformers, by Marius Aasan et al.
A Spitting Image: Modular Superpixel Tokenization in Vision Transformers
by Marius Aasan, Odd Kolbjørnsen, Anne Schistad Solberg, Adín Ramirez Rivera
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed modular superpixel tokenization strategy decouples tokenization and feature extraction in Vision Transformer (ViT) architectures, departing from traditional grid-based approaches. By using on-line content-aware tokenization and scale- and shape-invariant positional embeddings, the authors compare their method to patch-based tokenization and randomized partitions as baselines. The results demonstrate that the proposed approach improves attribution faithfulness, provides pixel-level granularity for zero-shot unsupervised dense prediction tasks, while maintaining classification performance. This modular framework enables ViTs to accommodate semantically-rich models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to divide images into small pieces, called tokens, in Vision Transformer (ViT) models. Instead of using the same old method that’s been used before, they developed a system that breaks down images into smaller groups based on their content. This allows for more accurate predictions and helps with tasks like recognizing objects in pictures without being specifically trained to do so. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Tokenization » Unsupervised » Vision transformer » Vit » Zero shot
