3 papers
cs.CV2026
SAIF: A Stability-Aware Inference Framework for Medical Image Segmentation with Segment Anything Model
Ke Wu, Shiqi Chen, Yiheng Zhong +5
Segment Anything Model (SAM) enable scalable medical image segmentation but suffer from inference-time instability when deployed as a frozen backbone. In practice, bounding-box pro…
cs.CV2025
SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Ruobing Li +8
The Segment Anything Model (SAM) is a promising foundation model for medical image segmentation, but its prompt-driven design is not directly suitable for fully automatic multi-cla…
cs.CV2024
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
Zhongxing Xu, Feilong Tang, Zhe Chen +5
The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilitie…