3 papers
cs.CV2026
LPT: Less-overfitting Prompt Tuning for Vision-Language Model
Chenhao Ding, Xinyuan Gao, Songlin Dong +5
Vision-language models (VLMs) have demonstrated exceptional generalization capabilities for downstream tasks. Due to its efficiency, prompt learning has gradually become a more eff…
cs.CV2025
Class-Independent Increment: An Efficient Approach for Multi-label Class-Incremental Learning
Chenhao Ding, Songlin Dong, Zhengdong Zhou +4
Current research on class-incremental learning primarily focuses on single-label classification tasks. However, real-world applications often involve multi-label scenarios, such as…
cs.CV2025
Diversity Covariance-Aware Prompt Learning for Vision-Language Models
Songlin Dong, Zhengdong Zhou, Chenhao Ding +3
Prompt tuning can further enhance the performance of visual-language models across various downstream tasks (e.g., few-shot learning), enabling them to better adapt to specific app…