9 papers
Class Incremental Medical Image Segmentation via Prototype-Guided Calibration and Dual-Aligned Distillation
Shengqian Zhu, Chengrong Yu, Qiang Wang +6
Class incremental medical image segmentation (CIMIS) aims to preserve knowledge of previously learned classes while learning new ones without relying on old-class labels. However,…
Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding
Jiaqi Tang, Jianmin Chen, Wei Wei +7
Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust ML…
Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement
Xiaogang Xu, Jian Wang, Yunfan Lu +5
Diffusion-based methods, leveraging pre-trained large models like Stable Diffusion via ControlNet, have achieved remarkable performance in several low-level vision tasks. However,…
Contextualized Representation Learning for Effective Human-Object Interaction Detection
Zhehao Li, Yucheng Qian, Chong Wang +3
Human-Object Interaction (HOI) detection aims to simultaneously localize human-object pairs and recognize their interactions. While recent two-stage approaches have made significan…
Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation
Shengqian Zhu, Jiafei Wu, Xiaogang Xu +5
Versatile medical image segmentation (VMIS) targets the segmentation of multiple classes, while obtaining full annotations for all classes is often impractical due to the time and…
DQEN: Dual Query Enhancement Network for DETR-based HOI Detection
Zhehao Li, Chong Wang, Yi Chen +4
Human-Object Interaction (HOI) detection focuses on localizing human-object pairs and recognizing their interactions. Recently, the DETR-based framework has been widely adopted in…