9 citations · 10 across the 5 of their papers we have counts for
5 papers
Show and Segment: Universal Medical Image Segmentation via In-Context Learning
Yunhe Gao, Di Liu, Zhuowei Li +4
Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made signific…
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering
Zhuowei Li, Haizhou Shi, Yunhe Gao +7
Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded content…
Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation
Qilong Zhangli, Di Liu, Abhishek Aich +2
Leveraging multiple training datasets to scale up image segmentation models is beneficial for increasing robustness and semantic understanding. Individual datasets have well-define…
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han +5
In-context Learning (ICL) empowers large language models (LLMs) to swiftly adapt to unseen tasks at inference-time by prefixing a few demonstration examples before queries. Despite…
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Yunhe Gao, Zhuowei Li, Di Liu +3
A major focus of clinical imaging workflow is disease diagnosis and management, leading to medical imaging datasets strongly tied to specific clinical objectives. This scenario has…