When Anomalies Depend on Context: Learning Conditional Compatibility for Anomaly Detection

• URL: http://arxiv.org/abs/2601.22868v1
• Date: Fri, 30 Jan 2026 11:48:20 GMT
• Title: When Anomalies Depend on Context: Learning Conditional Compatibility for Anomaly Detection
• Authors: Shashank Mishra, Didier Stricker, Jason Rambach,
• Abstract summary: Anomaly detection is often formulated under the assumption that abnormality is an intrinsic property of an observation, independent of context.<n>This assumption breaks down in many real-world settings, where the same object or action may be normal or anomalous depending on latent contextual factors.<n>We revisit emphcontextual anomaly detection, classically defined as context-dependent abnormality, and operationalize it in the visual domain.
• Score: 15.88746061477275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection is often formulated under the assumption that abnormality is an intrinsic property of an observation, independent of context. This assumption breaks down in many real-world settings, where the same object or action may be normal or anomalous depending on latent contextual factors (e.g., running on a track versus on a highway). We revisit \emph{contextual anomaly detection}, classically defined as context-dependent abnormality, and operationalize it in the visual domain, where anomaly labels depend on subject--context compatibility rather than intrinsic appearance. To enable systematic study of this setting, we introduce CAAD-3K, a benchmark that isolates contextual anomalies by controlling subject identity while varying context. We further propose a conditional compatibility learning framework that leverages vision--language representations to model subject--context relationships under limited supervision. Our method substantially outperforms existing approaches on CAAD-3K and achieves state-of-the-art performance on MVTec-AD and VisA, demonstrating that modeling context dependence complements traditional structural anomaly detection. Our code and dataset will be publicly released.

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