arXiv Computer Vision August 25, 2026 By Xianghong Fang, Yuan Yuan, Dehan Kong, Tim G. J. Rudner

VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers

Read the original on arXiv Computer Vision →

VQ-Transplant is a framework that allows new vector‑quantization (VQ) modules to be inserted into frozen, pre‑trained visual tokenizers without retraining the entire model. By preserving all encoder‑decoder parameters and adding a lightweight decoder adaptation trained for only five epochs on ImageNet‑1k, the method mitigates decoder‑quantization mismatch. Experiments show that VQ-Transplant achieves near state‑of‑the‑art reconstruction fidelity for industry‑level models such as VAR while cutting training costs by 95%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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