6 papers
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…
MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning
Tianhong Gao, Yannian Fu, Weiqun Wu +3
Large Language Models (LLMs), enhanced through agent tuning, have demonstrated remarkable capabilities in Chain-of-Thought (CoT) and tool utilization, significantly surpassing the…
KD-DETR: Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
Yu Wang, Xin Li, Shengzhao Weng +5
DETR is a novel end-to-end transformer architecture object detector, which significantly outperforms classic detectors when scaling up. In this paper, we focus on the compression o…
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…