Anomaly Preference Optimization reformulates anomalous image synthesis as preference learning with implicit alignment from real anomalies and a time-aware capacity allocation module for diffusion models to balance diversity and fidelity.
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
PostureObjectStitch generates assembly-aware anomaly images by decoupling multi-view features into high-frequency, texture and RGB components, modulating them temporally in a diffusion model, and applying conditional loss plus geometric priors to preserve correct component relationships.
AnomalyAgent uses tool-augmented reinforcement learning with self-reflection to generate realistic industrial anomalies, achieving better metrics than zero-shot methods on MVTec-AD.
citing papers explorer
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Anomaly-Preference Image Generation
Anomaly Preference Optimization reformulates anomalous image synthesis as preference learning with implicit alignment from real anomalies and a time-aware capacity allocation module for diffusion models to balance diversity and fidelity.
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PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios
PostureObjectStitch generates assembly-aware anomaly images by decoupling multi-view features into high-frequency, texture and RGB components, modulating them temporally in a diffusion model, and applying conditional loss plus geometric priors to preserve correct component relationships.
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AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning
AnomalyAgent uses tool-augmented reinforcement learning with self-reflection to generate realistic industrial anomalies, achieving better metrics than zero-shot methods on MVTec-AD.