Visual in-context learning (ICL) enables medical image segmentation models to adapt to new tasks using only a small set of annotated support images, without additional model fine-tuning. This makes visual ICL promising for clinical deployment, yet a fundamental limitation remains. Current ICL models lack the intrinsic...
Tiana-Tian-Ying Chen, Liang-Li Zhen, Yan-Yu Xu et al.· IEEE Transactions on Medical...· 0 citations
Long-horizon search requires agents to gather evidence across multiple steps and synthesize it into well-supported answers. The recent agent harnesses provide a natural and promising framework to support such long-running search processes. As interaction histories grow, one single agent in harnesses might get stuck and...
Shan-Yong Wang, Zhen-Wen Ji, Lei Jin et al.· 0 citations
Detailed image captioning requires accurate and comprehensive descriptions of fine-grained visual content, yet caption quality spans factual accuracy, information coverage, and clarity. Compared with conventional methods that rely mainly on high-quality supervision or holistic rewards, rubric-based reinforcement learni...
Zhen-Wen Ji, Lei Jin, Shanyong Wang et al.· 0 citations
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