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Found 2 papers, 1 papers with code
Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-$\mathrm{M^2}$DF)
In each view, we fuse the prompt embedding as consistent information with visual and the global or local temporal context to overcome the overlapping distribution of classes and outliers.
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
In the subsequent stage, we apply pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels.
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