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cs.LG2026
A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization
Xianghong Fang, Wenlong Mou, Yuan Yuan +2
Discrete visual tokenization, predominantly driven by vector, scalar, and product quantization, lacks a unified conceptual framework for understanding quantization tradeoffs. In th…
cs.CV2026
VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
Xianghong Fang, Yuan Yuan, Dehan Kong +1
Vector Quantization (VQ) underpins modern discrete visual tokenization. However, training quantization modules for state-of-the-art VQ-based models requires significant computation…
cs.CV2026
Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework
Xianghong Fang, Litao Guo, Hengchao Chen +8
The effectiveness of modern visual representation learning and autoregressive models critically depends on vector quantization (VQ), which discretizes continuous feature representa…