4 papers
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes
Xiaoqi Zhao, Youwei Pang, Shijie Chang +10
As large-scale foundation models trained on billions of image--mask pairs covering a vast diversity of scenes, objects, and contexts, SAM and its upgraded version, SAM~2, have sign…
UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model
Yilong Hu, Shijie Chang, Lihe Zhang +3
The Diffusion Probabilistic Model (DPM) has demonstrated remarkable performance across a variety of generative tasks. The inherent randomness in diffusion models helps address issu…
High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study
Shijie Chang, Lihe Zhang, Huchuan Lu
Existing few-shot segmentation (FSS) methods mainly focus on designing novel support-query matching and self-matching mechanisms to exploit implicit knowledge in pre-trained backbo…
Beyond Mask: Rethinking Guidance Types in Few-shot Segmentation
Shijie Chang, Youwei Pang, Xiaoqi Zhao +2
Existing few-shot segmentation (FSS) methods mainly focus on prototype feature generation and the query-support matching mechanism. As a crucial prompt for generating prototype fea…