AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection
Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to address the multi-class and continual learning demands of real-world environments. While Teacher-Student (TS) architectures are efficient, they remain unexplored for the Continual Setting. To bridge this gap, we propose AdapTS, a unified TS framework designed for multi-class and continual settings, optimized for edge deployment. AdapTS eliminates the need for two different architectures by utilizing a single shared frozen backbone and injecting lightweight trainable adapters into the student pathway. Training is enhanced via a segmentation-gui...
| Authors | Manuel Barusco, Davide Dalle Pezze, Francesco Borsatti et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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