Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice
Accurate and effective discrete image tokenization is crucial for long image sequence processing. However, current methods rigidly compress all content at a fixed rate, ignoring the variable information density of images and leading to either redundancy or information loss. Inspired by information entropy, we propose TaTok, a Theoretically grounded adaptive image Tokenization framework. We rigorously identify two key drawbacks in existing methods: information insufficiency when reconstructing images with patch tokens alone, and information redundancy among patch tokens. To address these, we introduce global tokens that model mutual information across patch tokens, and a Dynamic Token Filt...
| Authors | Xiusheng Huang, Xin Jiang, Jun Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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