2 citations · 10 across the 26 of their papers we have counts for
6 papers · 2 filters
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
Yun Guo, Xueyao Xiao, Yi Chang +2
Learning-based image deraining methods have made great progress. However, the lack of large-scale high-quality paired training samples is the main bottleneck to hamper the real ima…
1st Solution Places for CVPR 2023 UG+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence
Shengqi Xu, Shuning Cao, Haoyue Liu +3
In this technical report, we briefly introduce the solution of our team VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR 2023 UG+ Track 2.2. In t…
1st Solution Places for CVPR 2023 UG+ Challenge Track 2.1-Text Recognition through Atmospheric Turbulence
Shengqi Xu, Xueyao Xiao, Shuning Cao +2
In this technical report, we present the solution developed by our team VIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 of the CVPR 2023 UG+ chal…
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG+ Track 3
Yun Guo, Xueyao Xiao, Xiaoxiong Wang +3
In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG+ Track 3. In this task, we propose an efficient two-…
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow
Hanyu Zhou, Yi Chang, Gang Chen +1
Optical flow estimation has made great progress, but usually suffers from degradation under adverse weather. Although semi/full-supervised methods have made good attempts, the doma…
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
Hanyu Zhou, Yi Chang, Wending Yan +1
Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing method…