Conditional compatibility learning reframes anomaly detection as checking subject-context fit rather than global deviation, with CC-CLIP delivering state-of-the-art performance on contextual anomalies and competitive results on structural ones.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CAT framework reports 99.54% pixel-level AUROC on KolektorSDD2 with claimed superior generalization to three unseen defect datasets.
citing papers explorer
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Conditional Compatibility Learning for Context-Dependent Anomaly Detection
Conditional compatibility learning reframes anomaly detection as checking subject-context fit rather than global deviation, with CC-CLIP delivering state-of-the-art performance on contextual anomalies and competitive results on structural ones.
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Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection
CAT framework reports 99.54% pixel-level AUROC on KolektorSDD2 with claimed superior generalization to three unseen defect datasets.