most citedSlow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

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…

cs.CV2026

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…

cs.LG20261 cited

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…