MuRF fuses multi-resolution features from frozen vision foundation models at inference time to create stronger representations without any training.
Superad: A training-free anomaly classification and segmentation method for cvpr 2025 vand 3.0 workshop challenge track 1: Adapt & detect.arXiv preprint arXiv:2505.19750, 2025a
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SuperADD enhances SuperAD with DINOv3 backbone, overlapping patches, augmentations, better memory subsampling, and morphological closing to achieve higher F1 scores on MVTec AD 2 without class-specific tuning.
citing papers explorer
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MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models
MuRF fuses multi-resolution features from frozen vision foundation models at inference time to create stronger representations without any training.
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SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track
SuperADD enhances SuperAD with DINOv3 backbone, overlapping patches, augmentations, better memory subsampling, and morphological closing to achieve higher F1 scores on MVTec AD 2 without class-specific tuning.