{"ID":2879328,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16157","arxiv_id":"2508.16157","title":"Beyond Human-prompting: Adaptive Prompt Tuning with Semantic Alignment for Anomaly Detection","abstract":"Pre-trained Vision-Language Models (VLMs) have recently shown promise in detecting anomalies. However, previous approaches are fundamentally limited by their reliance on human-designed prompts and the lack of accessible anomaly samples, leading to significant gaps in context-specific anomaly understanding. In this paper, we propose \\textbf{A}daptive \\textbf{P}rompt \\textbf{T}uning with semantic alignment for anomaly detection (APT), a groundbreaking prior knowledge-free, few-shot framework and overcomes the limitations of traditional prompt-based approaches. APT uses self-generated anomaly samples with noise perturbations to train learnable prompts that capture context-dependent anomalies in different scenarios. To prevent overfitting to synthetic noise, we propose a Self-Optimizing Meta-prompt Guiding Scheme (SMGS) that iteratively aligns the prompts with general anomaly semantics while incorporating diverse synthetic anomaly. Our system not only advances pixel-wise anomaly detection, but also achieves state-of-the-art performance on multiple benchmark datasets without requiring prior knowledge for prompt crafting, establishing a robust and versatile solution for real-world anomaly detection.","short_abstract":"Pre-trained Vision-Language Models (VLMs) have recently shown promise in detecting anomalies. However, previous approaches are fundamentally limited by their reliance on human-designed prompts and the lack of accessible anomaly samples, leading to significant gaps in context-specific anomaly understanding. In this pape...","url_abs":"https://arxiv.org/abs/2508.16157","url_pdf":"https://arxiv.org/pdf/2508.16157v1","authors":"[\"Pi-Wei Chen\",\"Jerry Chun-Wei Lin\",\"Wei-Han Chen\",\"Jia Ji\",\"Zih-Ching Chen\",\"Feng-Hao Yeh\",\"Chao-Chun Chen\"]","published":"2025-08-22T07:26:56Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}