7 papers
PA-FAS: Towards Interpretable and Generalizable Multimodal Face Anti-Spoofing via Path-Augmented Reinforcement Learning
Yingjie Ma, Xun Lin, Yong Xu +2
Face anti-spoofing (FAS) has recently advanced in multimodal fusion, cross-domain generalization, and interpretability. With large language models and reinforcement learning (RL),…
SVC 2025: the First Multimodal Deception Detection Challenge
Xun Lin, Xiaobao Guo, Taorui Wang +5
Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown p…
FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models
Hongyang Wang, Yichen Shi, Zhuofu Tao +7
Face anti-spoofing (FAS) is crucial for protecting facial recognition systems from presentation attacks. Previous methods approached this task as a classification problem, lacking…
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival Prediction
Shuaiyu Zhang, Xun Lin, Rongxiang Zhang +5
The integration of pathologic images and genomic data for survival analysis has gained increasing attention with advances in multimodal learning. However, current methods often ign…
DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing
Jingyi Yang, Xun Lin, Zitong Yu +5
With the availability of diverse sensor modalities (i.e., RGB, Depth, Infrared) and the success of multi-modal learning, multi-modal face anti-spoofing (FAS) has emerged as a promi…
BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
Yingjie Ma, Zitong Yu, Xun Lin +2
In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challen…