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arXiv Computer Vision By Zixuan Fu, Lanqing Guo, Chong Wang, Binbin Song, Ding Liu, Bihan Wen

Improving Flexible Image Tokenizers for Autoregressive Image Generation

Read the original on arXiv Computer Vision →

The paper introduces ReToK, a flexible image tokenizer that uses redundant token padding and hierarchical semantic regularization to better utilize all tokens in autoregressive image generation. By activating tail tokens more frequently and aligning early token features with a pre‑trained vision model while gradually reducing regularization, ReToK mitigates the information concentration problem of traditional nested dropout tokenizers. Experiments on ImageNet 256×256 show that ReToK outperforms both flexible and fixed‑length tokenizers in generation quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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