54 citations · 55 across the 6 of their papers we have counts for
6 papers · 1 filter
Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval
Guosheng Zhang, Linkai Liu, Keyao Wang +3
Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus on sample-level objectives…
From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing
Haoyuan Zhang, Keyao Wang, Guosheng Zhang +11
Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classificat…
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models
Guosheng Zhang, Keyao Wang, Haixiao Yue +5
Face Anti-Spoofing (FAS) is essential for ensuring the security and reliability of facial recognition systems. Most existing FAS methods are formulated as binary classification tas…
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts
Sinan Du, Guosheng Zhang, Keyao Wang +7
Parameter-efficient transfer learning (PETL) has become a promising paradigm for adapting large-scale vision foundation models to downstream tasks. Typical methods primarily levera…
Cyclically Disentangled Feature Translation for Face Anti-spoofing
Haixiao Yue, Keyao Wang, Guosheng Zhang +4
Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary.…
Learning Generalized Spoof Cues for Face Anti-spoofing
Haocheng Feng, Zhibin Hong, Haixiao Yue +5
Many existing face anti-spoofing (FAS) methods focus on modeling the decision boundaries for some predefined spoof types. However, the diversity of the spoof samples including the…