[Paper] Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision

Source: arXiv - 2606.09670v1

Overview

Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated. Those variations that occur render anomaly detection methods unusable in many real-world scenarios. To address these limitations, we introduce three key contributions: (1) a visual prompting pipeline that isolates objects using foreground-background masking; (2) a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability; and (3) a data augmentation strategy leveraging diffusion-generated synthetic images to enhance anomaly detection performance. We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset by using the Masked Multiscale Reconstruction (MMR) model as our backbone.

Key Contributions

This paper presents research in the following areas:

• cs.CV
• cs.AI

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CV.

Authors

• Mateo Diaz-Bone
• Daniel Caraballo
• Florian Scheidegger
• Thomas Frick
• Mattia Rigotti
• Andrea Bartezzaghi
• Roy Assaf
• Niccolo Avogaro
• Yagmur G. Cinar
• Brown Ebouky
• Filip M. Janicki
• Piotr S. Kluska
• Cezary Skura
• Cristiano Malossi

Paper Information

• arXiv ID: 2606.09670v1
• Categories: cs.CV, cs.AI
• Published: June 8, 2026
• PDF: Download PDF