Research
Study finds discrete visual tokens can match continuous representations with Factorized Vector Quantization
The paper argues quantization error is not a fundamental limit and that bit budget drives performance. Its FVQ tokenizer reportedly matches continuous models like Qwen3-VL on multimodal understanding, including fine-grained OCR, and outperforms UniAR and Emu3-Chat on a nine-metric average.
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Hugging Face PapersResearchLead
Can Discrete Tokens Serve as Semantically Lossless Representations of Visual Signals?