9 papers
Text-Guided Multimodal Unified Industrial Anomaly Detection
Zewen Li, Shuo Ye, Zitong Yu +2
Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing unsupervised methods suffer…
Enhancing Adversarial Transferability by Balancing Exploration and Exploitation with Gradient-Guided Sampling
Zenghao Niu, Weicheng Xie, Siyang Song +3
Adversarial attacks present a critical challenge to deep neural networks' robustness, particularly in transfer scenarios across different model architectures. However, the transfer…
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
Zewen Li, Zitong Yu, Qilang Ye +3
The robust causal capability of Multimodal Large Language Models (MLLMs) hold the potential of detecting defective objects in Industrial Anomaly Detection (IAD). However, most trad…
Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
Yingxin Lai, Zitong Yu, Jun Wang +3
Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy,which we attribute to the ecological invalidity of training d…
MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection
Yaning Zhang, Tianyi Wang, Zitong Yu +3
The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable…
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models
Chaomeng Chen, Zitong Yu, Junhao Dong +4
Visual language models (VLMs) have shown remarkable capabilities in multimodal tasks but face challenges in maintaining fairness across demographic groups, particularly when deploy…