ViQ: Text-Aligned Visual Quantized Representations at Any Resolution
A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, representing images as discrete signals in the same way as text inevitably introduces severe information loss. Existing work struggles to balance low-level details and high-level semantics in discrete representations: reconstruction-oriented representations often lack semantic information, whereas semantically stronger features typically suffer from severe loss of detail. We present ViQ, a Visual Quantized Representations framework, which is designed to balance semantics and details in discrete representations while supporting inputs at native ...
| Authors | Xumin Yu, Zuyan Liu, Zhenyu Yang et al. |
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| Primary category | cs.CV |
| Published | 25 June 2026 |
| Last updated at source | 30 June 2026 |
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