most citedTimeline and Boundary Guided Diffusion Network for Video Shadow Detection

15 citations · 16 across the 8 of their papers we have counts for

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cs.CV2026

GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation

Sixiang Chen, Zhaohu Xing, Tian Ye +7

Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with ext…

cs.CV2024

Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors

Yunlong Lin, Zhenqi Fu, Kairun Wen +7

Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep ne…

cs.CV2024

Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint

Sixiang Chen, Tian Ye, Kai Zhang +3

Recent advancements in adverse weather restoration have shown potential, yet the unpredictable and varied combinations of weather degradations in the real world pose significant ch…

cs.CV202415 cited

Timeline and Boundary Guided Diffusion Network for Video Shadow Detection

Haipeng Zhou, Honqiu Wang, Tian Ye +5

Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD prob…

cs.CV2024

RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models

Haoyu Chen, Wenbo Li, Jinjin Gu +7

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual…

cs.CV20241 cited

AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement

Yunlong Lin, Tian Ye, Sixiang Chen +6

Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limit…