3 papers
cs.CV2026
U-shaped Multi-granularity Learning for Vision-Language Models
Biao Chen, Yunqian Yu, Xiangxu Zhao +3
The prompt learning paradigm for vision-language models is effective yet faces a granularity dilemma: global prompts lack fine-grained semantic awareness, while local prompts ignor…
cs.CV2025
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition
Kunshan Yang, Wenwei Luo, Yuguo Hu +3
Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based…
cs.CV2025
SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation
Zixi Wang, Xiangxu Zhao, Tonglan Xie +2
Domain shifts are critical issues that harm the performance of machine learning. Unsupervised Domain Adaptation (UDA) mitigates this issue but suffers when the domain shifts are st…