102 citations · 215 across the 22 of their papers we have counts for
10 papers · 1 filter
Improving Crowded Object Detection via Copy-Paste
Jiangfan Deng, Dewen Fan, Xiaosong Qiu +1
Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of…
Towards Privacy-Preserving, Real-Time and Lossless Feature Matching
Qiang Meng, Feng Zhou
Most visual retrieval applications store feature vectors for downstream matching tasks. These vectors, from where user information can be spied out, will cause privacy leakage if n…
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Qiang Meng, Xiaqing Xu, Xiaobo Wang +6
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g.…
Searching for Alignment in Face Recognition
Xiaqing Xu, Qiang Meng, Yunxiao Qin +4
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the…
Visible Feature Guidance for Crowd Pedestrian Detection
Zhida Huang, Kaiyu Yue, Jiangfan Deng +1
Heavy occlusion and dense gathering in crowd scene make pedestrian detection become a challenging problem, because it's difficult to guess a precise full bounding box according to…
Matching Guided Distillation
Kaiyu Yue, Jiangfan Deng, Feng Zhou
Feature distillation is an effective way to improve the performance for a smaller student model, which has fewer parameters and lower computation cost compared to the larger teache…
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
Zezheng Wang, Zitong Yu, Chenxu Zhao +5
Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. D…
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Zitong Yu, Chenxu Zhao, Zezheng Wang +5
Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is wea…
Deep Variational Luenberger-type Observer for Stochastic Video Prediction
Dong Wang, Feng Zhou, Zheng Yan +4
Considering the inherent stochasticity and uncertainty, predicting future video frames is exceptionally challenging. In this work, we study the problem of video prediction by combi…
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu +6
Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various pre…