arXiv cs.CV
· Papers
VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
arXiv:2607.19575v1 Announce Type: new Abstract: Vector Quantization (VQ) underpins modern discrete visual tokenization. However, training quantization modules for state-of-the-art VQ-based models requires significant computational resources which, in practice, all but prevents the development of novel, cutting-edge VQ