UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction

• URL: http://arxiv.org/abs/2606.29714v1
• Date: Mon, 29 Jun 2026 02:38:51 GMT
• Title: UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
• Abstract summary: We propose UniVAD v2, a two-sided support construction spanning unified visual anomaly detection.<n>Built on the component-patch divide-and-conditioned framework of UniVAD, UniVAD v2 strengthens the normal side with an Optimal Transport Modeling module (OTRM)<n>On the abnormal side, a Few-Shot Abnormal Reference module (FAR) converts abnormal references into rejection-side evidence for boundary adjustment.
• Score: 40.70728779828606
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the key challenge is to estimate an episode-specific boundary for an unseen target category from a small support set. Existing approaches mainly infer this boundary from normal-side evidence and provide limited abnormal-side evidence for deployment-specific tolerance. Within the normal side, they often struggle to jointly capture local correspondences and global support-query relations, making their boundaries less reliable for unseen anomalies. To address these issues, we propose UniVAD v2, a two-sided support-conditioned boundary construction framework for unified visual anomaly detection. Built on the component-patch divide-and-conquer framework of UniVAD, UniVAD v2 strengthens the normal side with an Optimal Transport-based Relational Modeling module (OTRM), which complements retrieval with support-query matching through transport-style allocation, and an Adaptive Coordination mechanism for Retrieval and Relational Modeling (ACRRM), which estimates episode-conditioned reliabilities to fuse the two sources of evidence. On the abnormal side, a Few-Shot Abnormal Reference module (FAR) converts optional abnormal references into rejection-side evidence for boundary adjustment. Experiments on six datasets spanning industrial, logical, and medical anomaly detection demonstrate strong cross-domain generalization. Under the 1N-shot protocol, UniVAD v2 improves the mean image-level AUC over UniVAD from 83.0\% to 84.5\%, and further reaches 85.7\% in the 1N+1A-shot setting. On the MVTec-AD Severity Split (MVTec-AD-SS), UniVAD v2 achieves 96.2\% image-level AUC and 96.9\% pixel-level AUC, showing that abnormal references enable controllable boundary customization without retraining.

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