3 citations · 3 across the 4 of their papers we have counts for
10 papers · 1 filter
Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)
Qi Zeng, Weide Liu, Bo Li +3
This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require lar…
VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results
Yixiao Li, Xin Li, Chris Wei Zhou +28
This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quali…
Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
Jianyang Zhang, Qianli Luo, Guowu Yang +4
Language Bottleneck Models (LBMs) are proposed to achieve interpretable image recognition by classifying images based on textual concept bottlenecks. However, current LBMs simply l…
Gaussian Mixture based Evidential Learning for Stereo Matching
Weide Liu, Xingxing Wang, Lu Wang +3
In this paper, we introduce a novel Gaussian mixture based evidential learning solution for robust stereo matching. Diverging from previous evidential deep learning approaches that…
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency
Weide Liu, Jingwen Hou, Xiaoyang Zhong +4
Deep learning-based brain tumor segmentation (BTS) models for multi-modal MRI images have seen significant advancements in recent years. However, a common problem in practice is th…
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
Rui Gong, Weide Liu, Zaiwang Gu +2
Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to integrate geometric insights into stereo matching algorithms have large…