Core Concepts
Introducing a novel codebook transfer framework with part-of-speech enhances image modeling by leveraging pretrained language models.
Abstract
The paper introduces a novel approach, VQCT, to transfer a well-trained codebook from language models to enhance Vector-Quantized Image Modeling (VQIM). By utilizing part-of-speech knowledge and semantic relationships from pretrained language models, the proposed framework aims to alleviate codebook collapse issues. Experimental results demonstrate superior performance over existing methods on four datasets. The method involves constructing vision-related codebooks, designing a codebook transfer network, and achieving cooperative optimization between codes.
Stats
Existing studies effectively address Vector-Quantized Image Modeling (VQIM) problem.
Experimental results show VQCT method achieves superior performance.
VQCT outperforms state-of-the-art methods on four datasets.
Quotes
"Neglecting the relationship between code vectors and priors is challenging."
"VQCT transfers abundant semantic knowledge from language models."
"Our method achieves robust codebook learning for VQIM."