Can Discrete Tokens Serve as Semantically Lossless Representations of Visual Signals?
The paper examines whether discrete visual tokens can preserve semantic information as well as continuous representations. It finds that quantization error is an unreliable proxy for multimodal understanding, and that the effective bit budget of discrete indices is a key performance factor. Existing quantizers saturate as codebooks are added, so the authors propose Factorized Vector Quantization (FVQ), which quantizes complementary fixed-dimensional feature views with parallel codebooks. The authors report that FVQ continues to improve at larger bit budgets and, at industrial scale, achieves strong multimodal understanding including fine-grained OCR.
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The paper examines whether discrete visual tokens can preserve semantic information as well as continuous representations. It finds that quantization error is an unreliable proxy for multimodal understanding, and that the effective bit budget of discrete indices is a key performance factor. Existing quantizers saturate as codebooks are added, so the authors propose Factorized Vector Quantization (FVQ), which quantizes complementary fixed-dimensional feature views with parallel codebooks. The authors report that FVQ continues to improve at larger bit budgets and, at industrial scale, achieves strong multimodal understanding including fine-grained OCR.
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Key findings from the stored brief
- Discrete representations with substantially larger quantization errors can achieve better multimodal understanding.
- Effective bit budget of discrete indices is a key determinant of downstream performance.
- FVQ uses symmetric parallel codebooks on complementary feature views to continue improving at larger bit budgets.
- Reported to outperform UniAR and Emu3-Chat on a nine-metric average by 22.8% and 59.2%.
Source claims not independently checked; the benchmark comparisons rely on the authors' industrial-scale training data and evaluation setup.
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Why this may matter
If discrete visual tokens can match continuous features, they could serve as a native, language-like interface for large multimodal foundation models trained with next-token prediction.
Limits and open questions
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