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Models/STEGO (ViT-B/8)

STEGO (ViT-B/8)

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

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

Medical2 results

Semantic SegmentationonCOCO-Stuff-27
Clustering [mIoU]· 2022-03-16
28.2
best: 54.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Unsupervised Semantic Segmentation by Distilling Feature Correspondences arXiv:2203.08414
Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2022-03-16
56.9
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
Unsupervised Semantic Segmentation by Distilling Feature Correspondences arXiv:2203.08414

Computer Vision2 results

Unsupervised Semantic SegmentationonCOCO-Stuff-27
Clustering [mIoU]· 2022-03-16
28.2
best: 54.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Unsupervised Semantic Segmentation by Distilling Feature Correspondences arXiv:2203.08414
Unsupervised Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2022-03-16
56.9
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
Unsupervised Semantic Segmentation by Distilling Feature Correspondences arXiv:2203.08414

Audio2 results

10-shot image generationonCOCO-Stuff-27
Clustering [mIoU]· 2022-03-16
28.2
best: 54.1 (DynaSeg - FSF (ResNet-18 FPN))
SOTA
Unsupervised Semantic Segmentation by Distilling Feature Correspondences arXiv:2203.08414
10-shot image generationonCOCO-Stuff-27
Clustering [Accuracy]· 2022-03-16
56.9
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
Unsupervised Semantic Segmentation by Distilling Feature Correspondences arXiv:2203.08414