Codebook Transfer With Vision-to-Language Translation for Vector Quantization.

Area of Science:

• Computer Vision
• Natural Language Processing
• Machine Learning

Background:

• Vector quantization (VQ) is crucial for image synthesis, representing images as discrete tokens.
• Current VQ methods struggle with codebook collapse due to learning from scratch and code-independent approaches.
• Pretrained language models possess valuable, yet underutilized, codebook information.

Purpose of the Study:

• To propose a novel codebook transfer framework (VQCT-VLT) for robust vector quantization.
• To leverage pretrained language models' codebooks to overcome challenges in VQ.
• To enhance VQ by aligning vision and language semantics for superior image synthesis.

Main Methods:

• Developed a codebook transfer framework (VQCT-VLT) utilizing pretrained language models and part-of-speech knowledge.
• Constructed a vision-related codebook using language model priors for effective codebook transfer.
• Integrated a vision-to-language translation module with image captions for vision-language-aligned codebook learning.

Main Results:

• The VQCT-VLT method demonstrates superior performance in image synthesis tasks compared to state-of-the-art VQ methods.
• Successfully transferred well-trained codebooks from language models, enhancing VQ robustness.
• Achieved vision-language alignment in codebook learning, improving semantic relevance.

Conclusions:

• VQCT-VLT offers a robust approach to vector quantization by transferring knowledge from pretrained language models.
• The framework effectively mitigates codebook collapse and improves image synthesis quality.
• Vision-language alignment is key for developing semantically meaningful and high-performing VQ systems.