34 citations · 228 across the 42 of their papers we have counts for
7 papers · 1 filter
Background-Mixed Augmentation for Weakly Supervised Change Detection
Rui Huang, Ruofei Wang, Qing Guo +4
Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in t…
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement
Shuangzhi Li, Zhijie Wang, Felix Juefei-Xu +3
Object detection through LiDAR-based point cloud has recently been important in autonomous driving. Although achieving high accuracy on public benchmarks, the state-of-the-art dete…
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions
Xuhong Ren, Jianlang Chen, Felix Juefei-Xu +5
Network architecture search (NAS), in particular the differentiable architecture search (DARTS) method, has shown a great power to learn excellent model architectures on the specif…
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Xiaofei Xie, Tianlin Li, Jian Wang +4
Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural…
Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining
Qing Guo, Jingyang Sun, Felix Juefei-Xu +4
Deraining is a significant and fundamental computer vision task, aiming to remove the rain streaks and accumulations in an image or video captured under a rainy day. Existing derai…
Masked Faces with Faced Masks
Jiayi Zhu, Qing Guo, Felix Juefei-Xu +3
Modern face recognition systems (FRS) still fall short when the subjects are wearing facial masks, a common theme in the age of respiratory pandemics. An intuitive partial remedy i…
ALA: Naturalness-aware Adversarial Lightness Attack
Yihao Huang, Liangru Sun, Qing Guo +5
Most researchers have tried to enhance the robustness of DNNs by revealing and repairing the vulnerability of DNNs with specialized adversarial examples. Parts of the attack exampl…