3 papers
cs.CV2025
Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +4
For CLIP-based prompt tuning, introducing more data as additional knowledge for enhancing fine-tuning process is proved to be an effective approach. Existing data amplification str…
cs.CV2025
MAO: Efficient Model-Agnostic Optimization of Prompt Tuning for Vision-Language Models
Haoyang Li, Siyu Zhou, Liang Wang +1
Though CLIP-based prompt tuning significantly enhances pre-trained Vision-Language Models, existing research focuses on reconstructing the model architecture, e.g., additional loss…
cs.CV2025
DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +3
The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simult…