2 citations · 3 across the 11 of their papers we have counts for
11 papers
TV-AudioRemover: Joint Text-Visual Guided Sound Removal with Multi-Task Hard-Mixture Curriculum
Xinyue Guo, Jianxuan Yang, Daiguo Zhou +4
Visual object removal can eliminate a target from video frames, yet its acoustic trace persists in the soundtrack, causing obvious audio-visual inconsistency. Existing video inpain…
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…
GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks
Feng Xie, Jiagao Hu, Fuhao Li +5
Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditi…
From Synthesis to Removal: Physics-Grounded Reflection Simulation and Diffusion-Based Video Dereflection
Zepeng Wang, Jiagao Hu, Fuhao Li +3
Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream vision tasks. Although single-image reflection removal has been ex…
PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media
Fuhao Li, Shaofeng You, Jiagao Hu +6
Evaluating object removal in images and videos remains challenging because the task is inherently one-to-many, yet existing metrics frequently disagree with human perception. Full-…
From Ideal to Real: Stable Video Object Removal under Imperfect Conditions
Jiagao Hu, Yuxuan Chen, Fuhao Li +4
Removing objects from videos remains difficult in the presence of real-world imperfections such as shadows, abrupt motion, and defective masks. Existing diffusion-based video inpai…
AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
Jiagao Hu, Daiguo Zhou, Danzhen Fu +6
Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-world video data in adverse wea…
NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods
Jie Cai, Kangning Yang, Zhiyuan Li +50
In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academ…
Controllable Pedestrian Video Editing for Multi-View Driving Scenarios via Motion Sequence
Danzhen Fu, Jiagao Hu, Daiguo Zhou +3
Pedestrian detection models in autonomous driving systems often lack robustness due to insufficient representation of dangerous pedestrian scenarios in training datasets. To addres…
Not All Pixels Are Equal: Learning Pixel Hardness for Semantic Segmentation
Xin Xiao, Daiguo Zhou, Jiagao Hu +2
Semantic segmentation has recently witnessed great progress. Despite the impressive overall results, the segmentation performance in some hard areas (e.g., small objects or thin pa…
Progressive refinement: a method of coarse-to-fine image parsing using stacked network
Jiagao Hu, Zhengxing Sun, Yunhan Sun +1
To parse images into fine-grained semantic parts, the complex fine-grained elements will put it in trouble when using off-the-shelf semantic segmentation networks. In this paper, f…