activity
20232026
most citedTowards Distribution-Agnostic Generalized Category Discovery

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Forgetting-Resistant and Lesion-Aware Source-Free Domain Adaptive Fundus Image Analysis with Vision-Language Model

Zheang Huai, Hui Tang, Hualiang Wang +1

Source-free domain adaptation (SFDA) aims to adapt a model trained in the source domain to perform well in the target domain, with only unlabeled target domain data and the source…

eess.IV2025

Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction

Jixiang Chen, Yiqun Lin, Yi Qin +2

Sparse-view computed tomography (CT) reduces radiation exposure by subsampling projection views, but conventional reconstruction methods produce severe streak artifacts with unders…

cs.LG2025

MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization

Huihui Xu, Yuanpeng Nie, Hualiang Wang +9

Medical Image Grounding (MIG), which involves localizing specific regions in medical images based on textual descriptions, requires models to not only perceive regions but also ded…

eess.IV2025

DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction

Yiqun Lin, Jixiang Chen, Hualiang Wang +4

Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-quality imaging raises significan…

eess.IV2024★ 1 cited

Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction

Yiqun Lin, Hualiang Wang, Jixiang Chen +1

Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these…

cs.CV2023★ 2 cited

Towards Distribution-Agnostic Generalized Category Discovery

Jianhong Bai, Zuozhu Liu, Hualiang Wang +7

Data imbalance and open-ended distribution are two intrinsic characteristics of the real visual world. Though encouraging progress has been made in tackling each challenge separate…