[2408.13516] Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2408.13516 (cs) [Submitted on 24 Aug 2024 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection Authors:Yujin Lee, Sewon Kim, Daeun Moon, Seoyoon Jang, Hyunsoo Yoon View a PDF of the paper titled Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection, by Yujin Lee and 4 other authors View PDF HTML (experimental) Abstract:Few-shot multi-class anomaly detection is crucial in real industrial settings, where only a few normal samples are available while numerous object types must be inspected. This setting is challenging as defect patterns vary widely across categories while normal samples remain scarce. Existing vision-language model-based approaches typically depend on class-specific anomaly descriptions or auxiliary modules, limiting both scalability and computational efficiency. In this work, we propose AnoPLe, a lightweight multimodal prompt learning framework that removes reliance on anomaly-type textual descriptions and avoids any external modules. AnoPLe employs bidirectional interactions between textual and visual prompts, allowing class semantics and instance-level cues to refine one another and form class-conditioned representations that capture shared normal patterns across categories. To enhance localization, we design a scale-aware pr...