2 citations · 3 across the 14 of their papers we have counts for
5 papers · 2 filters
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +3
Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous metho…
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…
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +2
Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods ofte…
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…