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Models/CAUSE (DINOv2, ViT-B/14)

CAUSE (DINOv2, ViT-B/14)

Reported on 9 benchmarks across 3 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical3 results

Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2023-10-11
78
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Causal Unsupervised Semantic Segmentation arXiv:2310.07379
Semantic SegmentationonCOCO-Stuff-27
Clustering [mIoU]· 2023-10-11
45.3
best: 54.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Causal Unsupervised Semantic Segmentation arXiv:2310.07379
Semantic SegmentationonPASCAL VOC 2012 val
Clustering [mIoU]· 2023-10-11
53.2
best: 53.4 (CAUSE (iBOT, ViT-B/16))
Causal Unsupervised Semantic Segmentation arXiv:2310.07379

Computer Vision3 results

Unsupervised Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2023-10-11
78
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Causal Unsupervised Semantic Segmentation arXiv:2310.07379
Unsupervised Semantic SegmentationonCOCO-Stuff-27
Clustering [mIoU]· 2023-10-11
45.3
best: 54.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Causal Unsupervised Semantic Segmentation arXiv:2310.07379
Unsupervised Semantic SegmentationonPASCAL VOC 2012 val
Clustering [mIoU]· 2023-10-11
53.2
best: 53.4 (CAUSE (iBOT, ViT-B/16))
Causal Unsupervised Semantic Segmentation arXiv:2310.07379

Audio3 results

10-shot image generationonCOCO-Stuff-27
Clustering [Accuracy]· 2023-10-11
78
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Causal Unsupervised Semantic Segmentation arXiv:2310.07379
10-shot image generationonCOCO-Stuff-27
Clustering [mIoU]· 2023-10-11
45.3
best: 54.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Causal Unsupervised Semantic Segmentation arXiv:2310.07379
10-shot image generationonPASCAL VOC 2012 val
Clustering [mIoU]· 2023-10-11
53.2
best: 53.4 (CAUSE (iBOT, ViT-B/16))
Causal Unsupervised Semantic Segmentation arXiv:2310.07379