5 papers
Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners
Xinyue Liu, Jianyuan Wang, Biao Leng +1
Knowledge distillation (KD) has been widely studied in unsupervised image Anomaly Detection (AD), but its application to unsupervised multimodal AD remains underexplored. Existing…
ResCLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment
Xinyue Liu, Jianyuan Wang, Biao Leng +1
Few-shot Generalist Anomaly Detection requires models to generalize to novel categories without retraining, posing significant challenges in real-world scenarios with scarce sample…
Isolating to Harness: Cross-Division Distillation for Fully Unsupervised Anomaly Detection
Xinyue Liu, Jianyuan Wang, Biao Leng +1
Fully Unsupervised Anomaly Detection (FUAD) addresses the practical scenario where training data is contaminated with unlabeled anomalies. This setting critically challenges conven…
Unlocking the Potential of Reverse Distillation for Anomaly Detection
Xinyue Liu, Jianyuan Wang, Biao Leng +1
Knowledge Distillation (KD) is a promising approach for unsupervised Anomaly Detection (AD). However, the student network's over-generalization often diminishes the crucial represe…
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
Xinyue Liu, Jianyuan Wang, Biao Leng +1
Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the differenc…