Discrete visual tokens represent vision in a discrete form analogous to language, offering a native interface for multimodal modeling under a shared next-token prediction objective. However, quantizat
Factorized Vector Quantization improves discrete visual token quality by using parallel codebooks on complementary feature views, enabling discrete representations to match continuous ones in multimodal understanding.
Discrete visual tokens represent vision in a discrete form analogous to language, offering a native interface for multimodal modeling under a shared next-token prediction objective. However, quantization error is widely assumed to cause unavoidable semantic loss, fundamentally limiting the semantic fidelity of discrete visual tokens and positioning continuous visual representations as an intrinsic upper bound. Using multimodal understanding as a proxy for semantic fidelity, we revisit this assumption and find quantization error unreliable, as discrete representations with substantially larger errors can achieve better understanding. Instead, the effective bit budget of discrete indices…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Can Discrete Tokens Serve as Semantically Lossless Representations of Visual Signals? was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 23 checks on 3 different days. Its best position was #3 of 30, reached on Oct 30, 05:28 UTC. It dropped off the list after Oct 30, 05:28 UTC.
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It was first seen on Oct 28, 2026 and has been on the list on 3 days (23 checks) since.