1 citations · 2 across the 11 of their papers we have counts for
5 papers · 2 filters
La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detection
Hang Zou, Chenxi Du, Hui Zhang +4
Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separa…
CRL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval
Zhigang Chen, Benjia Zhou, Yiqing Huang +6
Sign Language Representation Learning (SLRL) is crucial for a range of sign language-related downstream tasks such as Sign Language Translation (SLT) and Sign Language Retrieval (S…
Unified Physical-Digital Attack Detection Challenge
Haocheng Yuan, Ajian Liu, Junze Zheng +6
Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existin…
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing
Ajian Liu, Shuai Xue, Jianwen Gan +5
Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. Existing methods either rely on domain labels to align domain-i…
Unified Physical-Digital Face Attack Detection
Hao Fang, Ajian Liu, Haocheng Yuan +8
Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at…