@inproceedings{
guotao2024lgvq,
title={{LG}-{VQ}: Language-Guided Codebook Learning},
author={Liang Guotao and Baoquan Zhang and Yaowei Wang and Yunming Ye and Xutao Li and Wanghuaibin and Luo Chuyao and kolaye and luolinfeng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=vA4s3kN4QE}
}
Vector quantization (VQ) is a key technique in high-resolution and high-fidelity image synthesis, which aims to learn a codebook to encode an image with a sequence of discrete codes and then generate an image in an auto-regression manner. 
  Although existing methods have shown superior performance, most methods prefer to learn a single-modal codebook (\emph{e.g.}, image), resulting in suboptimal performance when the codebook is applied to multi-modal downstream tasks (\emph{e.g.}, text-to-image, image captioning) due to the existence of modal gaps.
  In this paper, we propose a novel language-guided codebook learning framework, called LG-VQ, which aims to learn a codebook that can be aligned with the text to improve the performance of multi-modal downstream tasks. Specifically, we first introduce pre-trained text semantics as prior knowledge, then design two novel alignment modules (\emph{i.e.}, Semantic Alignment Module, and Relationship Alignment Module) to transfer such prior knowledge into codes for achieving codebook text alignment.   
  In particular, our LG-VQ method is model-agnostic, which can be easily integrated into existing VQ models. Experimental results show that our method achieves superior performance on reconstruction and various multi-modal downstream tasks.
LG-VQ: Language-Guided Codebook Learning
Liang Guotao Baoquan Zhang Yaowei Wang Yunming Ye Xutao Li Wanghuaibin Luo Chuyao kolaye luolinfeng