3 papers
cs.CV2026
S2FT: Parameter-Efficient Fine-Tuning in Sparse Spectrum Domain
Baoquan Zhang, Zhehao Yu, Lisai Zhang +5
Parameter Efficient Fine-Tuning (PEFT) is a key technique for adapting a large pretrained model to downstream tasks by fine-tuning only a small number of parameters. Recent methods…
cs.CV2025
Towards Improved Text-Aligned Codebook Learning: Multi-Hierarchical Codebook-Text Alignment with Long Text
Guotao Liang, Baoquan Zhang, Zhiyuan Wen +4
Image quantization is a crucial technique in image generation, aimed at learning a codebook that encodes an image into a discrete token sequence. Recent advancements have seen rese…
cs.CV2024
Codebook Transfer with Part-of-Speech for Vector-Quantized Image Modeling
Baoquan Zhang, Huaibin Wang, Luo Chuyao +5
Vector-Quantized Image Modeling (VQIM) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effec…