270 citations · 623 across the 19 of their papers we have counts for
19 papers
BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition
Jiahong Zhang, Lihong Cao, Qiuxia Lai +2
The partially occluded image recognition (POIR) problem has been a challenge for artificial intelligence for a long time. A common strategy to handle the POIR problem is using the…
Dual Complementary Dynamic Convolution for Image Recognition
Longbin Yan, Yunxiao Qin, Shumin Liu +1
As a powerful engine, vanilla convolution has promoted huge breakthroughs in various computer tasks. However, it often suffers from sample and content agnostic problems, which limi…
Consistency Regularization for Deep Face Anti-Spoofing
Zezheng Wang, Zitong Yu, Xun Wang +7
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems. Empirically, given an image, a model with more consistent output on different views of this imag…
Meta-Teacher For Face Anti-Spoofing
Yunxiao Qin, Zitong Yu, Longbin Yan +3
Face anti-spoofing (FAS) secures face recognition from presentation attacks (PAs). Existing FAS methods usually supervise PA detectors with handcrafted binary or pixel-wise labels.…
Adversarial Attack across Datasets
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi +2
Existing transfer attack methods commonly assume that the attacker knows the training set (e.g., the label set, the input size) of the black-box victim models, which is usually unr…
Training Meta-Surrogate Model for Transferable Adversarial Attack
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi +1
We consider adversarial attacks to a black-box model when no queries are allowed. In this setting, many methods directly attack surrogate models and transfer the obtained adversari…
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.…
Deep Learning for Face Anti-Spoofing: A Survey
Zitong Yu, Yunxiao Qin, Xiaobai Li +3
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more real…
Dual-Cross Central Difference Network for Face Anti-Spoofing
Zitong Yu, Yunxiao Qin, Hengshuang Zhao +2
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Recently, central difference convolution (CDC) has shown its excellent representation capacity for…
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…
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
Zitong Yu, Jun Wan, Yunxiao Qin +3
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Existing methods heavily rely on the expert-designed networks, which may lead to a sub-optimal sol…
Layer-Wise Adaptive Updating for Few-Shot Image Classification
Yunxiao Qin, Weiguo Zhang, Zezheng Wang +2
Few-shot image classification (FSIC), which requires a model to recognize new categories via learning from few images of these categories, has attracted lots of attention. Recently…
Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
Zitong Yu, Yunxiao Qin, Xiaobai Li +4
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, w…
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…
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…
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning
Yunxiao Qin, Weiguo Zhang, Chenxu Zhao +5
Recently, meta-learning has been shown as a promising way to solve few-shot learning. In this paper, inspired by the human cognition process which utilizes both prior-knowledge and…
Representation based and Attention augmented Meta learning
Yunxiao Qin, Chenxu Zhao, Zezheng Wang +3
Deep learning based computer vision fails to work when labeled images are scarce. Recently, Meta learning algorithm has been confirmed as a promising way to improve the ability of…
Exploiting temporal and depth information for multi-frame face anti-spoofing
Zezheng Wang, Chenxu Zhao, Yunxiao Qin +4
Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nev…