Unlocking Visual Anomaly Detection: Navigating Challenges and Pioneering with Vision-Language Models

Visual anomaly detection (VAD) is pivotal for ensuring quality in manufacturing, medical imaging, and safety inspections, yet it continues to face challenges such as data scarcity, domain shifts, and the need for precise localization and reasoning. This seminar explores VAD fundamentals, core challenges, and recent advancements leveraging vision-language models and multimodal large language models (MLLMs). We contrast CLIP-based methods for efficient zero/few-shot detection with MLLM-driven reasoning for explainable, threshold-free outcomes. Drawing from recent studies, we highlight emerging trends, benchmarks, and future directions toward building adaptable, real-world VAD systems. This talk is designed for researchers and practitioners interested in AI-driven inspection and next-generation multimodal approaches. Resources * ROADS: https://openaccess.thecvf.com/content/WACV2025/papers/Kashiani_ROADS_Robust_Prompt-Driven_Multi-Class_Anomaly_Detection_under_Domain_Shift_WACV_2025_paper.pdf * WinCLIP: https://arxiv.org/abs/2303.14814 * AnomalyCLIP - https://arxiv.org/pdf/2310.18961 * AnomalyGPT - https://arxiv.org/abs/2308.15366 * MMAD: https://arxiv.org/abs/2410.09453 About the Speaker Hossein Kashiani is a fourth-year Ph.D. student at Clemson University. His research focuses on developing generalizable and trustworthy AI systems, with publications in top venues such as CVPR, WACV, ICIP, IJCB, and TBIOM. His work spans diverse applications, including anomaly detection, media forensics, biometrics, healthcare, and visual perception. #computervision #ai #artificialintelligence #machinelearning