10 papers
Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
Zongye Hu, Weiqing Luo, Yanjie Fu +3
At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its c…
PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentation
Bin Xu, Yazhou Zhu, Haofeng Zhang
Few-Shot Medical Image Segmentation (FSMIS) offers a powerful solution to data scarcity but struggles to generalize across different imaging modalities. This performance collapse s…
Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching
Sujun Sun, Mingwu Ren, Haofeng Zhang
Cross-domain Few-shot Segmentation (CD-FSS) aims to transfer knowledge learned from source domain to distinct target domains, segmenting unseen target classes with only a few annot…
Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation
Sujun Sun, Mingwu Ren, Haofeng Zhang
Cross-domain Few-shot Segmentation (CD-FSS) aims to learn generalizable segmentation capability from abundant annotated samples in the source domain, enabling accurate segmentation…
Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation
Weiqing Luo, Zongye Hu, Xiao Wang +3
Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic relevance or surface-level sim…
Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation
Feifan Song, Yuntian Bo, Haofeng Zhang
Cross-domain few-shot medical image segmentation (CD-FSMIS) requires a model to generalise simultaneously to novel anatomical categories and unseen imaging domains from only a hand…
Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting
Yuntian Bo, Yazhou Zhu, Piotr Koniusz +1
Conventional few-shot medical image segmentation (FSMIS) approaches face performance bottlenecks that hinder broader clinical applicability. Although the Segment Anything Model (SA…
Bridging Granularity Gaps: Hierarchical Semantic Learning for Cross-domain Few-shot Segmentation
Sujun Sun, Haowen Gu, Cheng Xie +3
Cross-domain Few-shot Segmentation (CD-FSS) aims to segment novel classes from target domains that are not involved in training and have significantly different data distributions…
MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation
Yazhou Zhu, Haofeng Zhang
Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) is a potential solution for segmenting medical images with limited annotation using knowledge from other domains. The si…
Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation
Jianchao Jiang, Haofeng Zhang
Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. However, most existing prototype-b…