LG-VQ: Language-Guided Codebook Learning
<|reference_start|>LG-VQ: Language-Guided Codebook Learning: 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.<|reference_end|>
@article{liang2024lg-vq:,
    title={LG-VQ: Language-Guided Codebook Learning},
    author={Guotao Liang, Baoquan Zhang, Yaowei Wang, Xutao Li, Yunming Ye,
  Huaibin Wang, Chuyao Luo, Kola Ye, linfeng Luo},
    journal={NeurIPS 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14206},
    primaryClass={cs.CV}
}