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Models/HP

HP

Reported on 9 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.

Medical3 results

Semantic SegmentationonPotsdam-3
Accuracy· 2023-03-27
82.4
best: 83.3 (PriMaPs-EM+HP (DINO ViT-B/8))
SOTA
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014
Semantic SegmentationonCityscapes test
Accuracy· 2023-03-27
80.1
best: 84.3 (ViCE)
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014
Semantic SegmentationonCityscapes test
mIoU· 2023-03-27
18.4
best: 67.3 (EDANet)
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014

Computer Vision3 results

Unsupervised Semantic SegmentationonPotsdam-3
Accuracy· 2023-03-27
82.4
best: 83.3 (PriMaPs-EM+HP (DINO ViT-B/8))
SOTA
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014
Unsupervised Semantic SegmentationonCityscapes test
Accuracy· 2023-03-27
80.1
best: 84.3 (ViCE)
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014
Unsupervised Semantic SegmentationonCityscapes test
mIoU· 2023-03-27
18.4
best: 26.8 (CUPS)
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014

Audio3 results

10-shot image generationonPotsdam-3
Accuracy· 2023-03-27
82.4
best: 83.3 (PriMaPs-EM+HP (DINO ViT-B/8))
SOTA
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014
10-shot image generationonCityscapes test
Accuracy· 2023-03-27
80.1
best: 84.3 (ViCE)
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014
10-shot image generationonCityscapes test
mIoU· 2023-03-27
18.4
best: 67.3 (EDANet)
Leveraging Hidden Positives for Unsupervised Semantic Segmentation arXiv:2303.15014