activity
20182022
most citedNAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing

270 citations · 310 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV2022

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…

cs.LG20214 cited

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…

cs.CV202123 cited

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.…

cs.CV2021

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…

cs.CV20213 cited

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…

cs.CV2020270 cited

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…