1 citations · 1 across the 8 of their papers we have counts for
8 papers
S3VD: Semantic-Guidance Spatio-Temporal Scanning for Video Deraining
Kui Jiang, Yiang Chen, Yan Luo +3
Heavy rainfall severely degrades outdoor videos by corrupting high-frequency details and introducing motion blur, critically undermining the reliability of visual tasks. Recently,…
LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results
Zewei He, Xi Tong, Yu Chen +49
This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequently encountered practical pr…
RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics
Kui Jiang, Runzhe Li, Zhaocheng Yu +3
Video deraining aims to recover clean visual content from rainy videos for reliable perception under adverse weather. Existing methods mainly rely on RGB sequences and temporal red…
NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Suhang Yao +95
This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this c…
Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability
Xingyu Xie, Zhaochen Yu, Yue Liao +3
Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent pattern during decoding: within…
Derain-Agent: A Plug-and-Play Agent Framework for Rainy Image Restoration
Zhaocheng Yu, Xiang Chen, Runzhe Li +4
While deep learning has advanced single-image deraining, existing models suffer from a fundamental limitation: they employ a static inference paradigm that fails to adapt to the co…
Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining
Zhaocheng Yu, Kui Jiang, Junjun Jiang +3
Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining method…
Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather
Kui Jiang, Jing Cao, Zhaocheng Yu +2
Monocular depth estimation is critical for applications such as autonomous driving and scene reconstruction. While existing methods perform well under normal scenarios, their perfo…