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.

Authors

• Guotao Liang Harbin Institute of Technology, Shenzhen
• Baoquan Zhang Harbin Institute of Technology, Shenzhen
• Yaowei Wang Pengcheng Laboratory Harbin Institute of Technology, Shenzhen
• Xutao Li Harbin Institute of Technology, Shenzhen
• Yunming Ye Harbin Institute of Technology, Shenzhen
• Huaibin Wang
• Chuyao Luo Harbin Institute of Technology, Shenzhen
• Kola Ye
• Linfeng Luo

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