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Models/SGSeg

SGSeg

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-3
Pixel Accuracy· 2022-10-21
74.6
best: 80.3 (SAN)
SOTA
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks arXiv:2210.11810
Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2022-10-21
55.7
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks arXiv:2210.11810

Computer Vision2 results

Unsupervised Semantic SegmentationonCOCO-Stuff-3
Pixel Accuracy· 2022-10-21
74.6
best: 80.3 (SAN)
SOTA
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks arXiv:2210.11810
Unsupervised Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2022-10-21
55.7
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks arXiv:2210.11810

Audio2 results

10-shot image generationonCOCO-Stuff-3
Pixel Accuracy· 2022-10-21
74.6
best: 80.3 (SAN)
SOTA
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks arXiv:2210.11810
10-shot image generationonCOCO-Stuff-27
Clustering [Accuracy]· 2022-10-21
55.7
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks arXiv:2210.11810