1 citations · 2 across the 7 of their papers we have counts for
7 papers
Degradation-Aware Adaptive Context Gating for Unified Image Restoration
Lei He, Jielei Chu, Fengmao Lv +4
Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradation-Aware Adaptive Context Ga…
Modality-Aware Feature Matching in Visual and Vision-Language Applications: A Comprehensive Survey
Weide Liu, Wei Zhou, Jun Liu +4
Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM. This survey comprehensi…
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…
ELFNet: Evidential Local-global Fusion for Stereo Matching
Jieming Lou, Weide Liu, Zhuo Chen +2
Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation. Addit…
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Weide Liu, Zhonghua Wu, Yang Zhao +4
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome th…