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Models/n-CPS (ResNet-50)

n-CPS (ResNet-50)

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Medical3 results

Semantic SegmentationonCityscapes 6.25% labeled
Validation mIoU· 2021-12-14
76.08
best: 83.6 (UniMatch V2 (DINOv2-B))
SOTA
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation arXiv:2112.07528
Semantic SegmentationonPascal VOC 2012 6.25% labeled
Validation mIoU· 2021-12-14
72.03
best: 82 (SemiOVS (w/ PrevMatch, ResNet-101))
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation arXiv:2112.07528
Semantic SegmentationonPASCAL VOC 2012 25% labeled
Validation mIoU· 2021-12-14
75.85
best: 81.3 (SemiOVS (w/ PrevMatch, ResNet-101))
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation arXiv:2112.07528

Audio3 results

10-shot image generationonCityscapes 6.25% labeled
Validation mIoU· 2021-12-14
76.08
best: 83.6 (UniMatch V2 (DINOv2-B))
SOTA
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation arXiv:2112.07528
10-shot image generationonPascal VOC 2012 6.25% labeled
Validation mIoU· 2021-12-14
72.03
best: 82 (SemiOVS (w/ PrevMatch, ResNet-101))
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation arXiv:2112.07528
10-shot image generationonPASCAL VOC 2012 25% labeled
Validation mIoU· 2021-12-14
75.85
best: 81.3 (SemiOVS (w/ PrevMatch, ResNet-101))
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation arXiv:2112.07528