TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection

• URL: http://arxiv.org/abs/2602.03594v1
• Date: Tue, 03 Feb 2026 14:48:11 GMT
• Title: TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
• Authors: Alireza Salehi, Ehsan Karami, Sepehr Noey, Sahand Noey, Makoto Yamada, Reshad Hosseini, Mohammad Sabokrou,
• Abstract summary: Anomaly detection identifies departures from expected behavior in safety-critical settings.<n>Our pipeline improves image-level performance by 1.1-3.9% and pixel-level by 1.5-6.9% across seven industrial datasets.
• Score: 19.691698434869657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection identifies departures from expected behavior in safety-critical settings. When target-domain normal data are unavailable, zero-shot anomaly detection (ZSAD) leverages vision-language models (VLMs). However, CLIP's coarse image-text alignment limits both localization and detection due to (i) spatial misalignment and (ii) weak sensitivity to fine-grained anomalies; prior work compensates with complex auxiliary modules yet largely overlooks the choice of backbone. We revisit the backbone and use TIPS-a VLM trained with spatially aware objectives. While TIPS alleviates CLIP's issues, it exposes a distributional gap between global and local features. We address this with decoupled prompts-fixed for image-level detection and learnable for pixel-level localization-and by injecting local evidence into the global score. Without CLIP-specific tricks, our TIPS-based pipeline improves image-level performance by 1.1-3.9% and pixel-level by 1.5-6.9% across seven industrial datasets, delivering strong generalization with a lean architecture. Code is available at github.com/AlirezaSalehy/Tipsomaly.

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