57 citations · 260 across the 30 of their papers we have counts for
8 papers · 1 filter
Benchmarking Shadow Removal for Facial Landmark Detection and Beyond
Lan Fu, Qing Guo, Felix Juefei-Xu +4
Facial landmark detection is a very fundamental and significant vision task with many important applications. In practice, facial landmark detection can be affected by a lot of nat…
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
Yujun Zhang, Lei Zhu, Wei Feng +5
Lane detection plays a key role in autonomous driving. While car cameras always take streaming videos on the way, current lane detection works mainly focus on individual images (fr…
From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
Ye Liu, Lei Zhu, Shunda Pei +5
Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing metho…
Adversarial Relighting Against Face Recognition
Qian Zhang, Qing Guo, Ruijun Gao +3
Deep face recognition (FR) has achieved significantly high accuracy on several challenging datasets and fosters successful real-world applications, even showing high robustness to…
CarveNet: Carving Point-Block for Complex 3D Shape Completion
Qing Guo, Zhijie Wang, Felix Juefei-Xu +4
3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e.g., high-curvature, concave/convex, and hollowed-o…
Sparta: Spatially Attentive and Adversarially Robust Activation
Qing Guo, Felix Juefei-Xu, Changqing Zhou +3
Adversarial training (AT) is one of the most effective ways for improving the robustness of deep convolution neural networks (CNNs). Just like common network training, the effectiv…
AdvHaze: Adversarial Haze Attack
Ruijun Gao, Qing Guo, Felix Juefei-Xu +2
In recent years, adversarial attacks have drawn more attention for their value on evaluating and improving the robustness of machine learning models, especially, neural network mod…
Auto-Exposure Fusion for Single-Image Shadow Removal
Lan Fu, Changqing Zhou, Qing Guo +5
Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful st…