The paper "Codebook Transfer with Part-of-Speech for Vector-Quantized Image Modeling" addresses the issue of codebook collapse in Vector-Quantized Image Modeling (VQIM), a challenging task in machine learning that aims to encode images into discrete token sequences. The authors propose a novel framework called VQCT, which leverages pre-trained language models and part-of-speech (POS) knowledge to enhance VQIM codebook learning. The key idea is to transfer the semantic relationships between codes from language models to VQIM, allowing for cooperative optimization of code vectors during training. This approach helps to alleviate the codebook collapse issue, where only a few code vectors are optimized while most remain unchanged. The VQCT framework consists of an encoder, a codebook transfer module, and a decoder. The codebook transfer module uses a graph convolution-based network to model the relationships between adjective and noun codebooks, ensuring that all code vectors can be optimized. Experimental results on four datasets (ADE20K, CelebA-HQ, CUB-200, and MSCOCO) demonstrate the effectiveness of VQCT in improving image reconstruction quality and reducing codebook collapse. The method is also shown to be effective in semantic image synthesis tasks.The paper "Codebook Transfer with Part-of-Speech for Vector-Quantized Image Modeling" addresses the issue of codebook collapse in Vector-Quantized Image Modeling (VQIM), a challenging task in machine learning that aims to encode images into discrete token sequences. The authors propose a novel framework called VQCT, which leverages pre-trained language models and part-of-speech (POS) knowledge to enhance VQIM codebook learning. The key idea is to transfer the semantic relationships between codes from language models to VQIM, allowing for cooperative optimization of code vectors during training. This approach helps to alleviate the codebook collapse issue, where only a few code vectors are optimized while most remain unchanged. The VQCT framework consists of an encoder, a codebook transfer module, and a decoder. The codebook transfer module uses a graph convolution-based network to model the relationships between adjective and noun codebooks, ensuring that all code vectors can be optimized. Experimental results on four datasets (ADE20K, CelebA-HQ, CUB-200, and MSCOCO) demonstrate the effectiveness of VQCT in improving image reconstruction quality and reducing codebook collapse. The method is also shown to be effective in semantic image synthesis tasks.