Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections

• URL: http://arxiv.org/abs/2504.11055v1
• Date: Tue, 15 Apr 2025 10:42:25 GMT
• Title: Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections
• Authors: Alireza Salehi, Mohammadreza Salehi, Reshad Hosseini, Cees G. M. Snoek, Makoto Yamada, Mohammad Sabokrou,
• Abstract summary: Anomaly Detection (AD) involves identifying deviations from normal data distributions.<n>We propose a novel approach that conditions the prompts of the text encoder based on image context extracted from the vision encoder.<n>Our method achieves state-of-the-art performance, improving performance by 2% to 29% across different metrics on 14 datasets.
• Score: 50.343419243749054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly Detection (AD) involves identifying deviations from normal data distributions and is critical in fields such as medical diagnostics and industrial defect detection. Traditional AD methods typically require the availability of normal training samples; however, this assumption is not always feasible, as collecting such data can be impractical. Additionally, these methods often struggle to generalize across different domains. Recent advancements, such as AnomalyCLIP and AdaCLIP, utilize the zero-shot generalization capabilities of CLIP but still face a performance gap between image-level and pixel-level anomaly detection. To address this gap, we propose a novel approach that conditions the prompts of the text encoder based on image context extracted from the vision encoder. Also, to capture fine-grained variations more effectively, we have modified the CLIP vision encoder and altered the extraction of dense features. These changes ensure that the features retain richer spatial and structural information for both normal and anomalous prompts. Our method achieves state-of-the-art performance, improving performance by 2% to 29% across different metrics on 14 datasets. This demonstrates its effectiveness in both image-level and pixel-level anomaly detection.

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