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Models/PiCIE

PiCIE

Reported on 9 benchmarks across 3 tasks · 1 paper · 4 SOTA

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

Medical3 results

Semantic SegmentationonCityscapes test
Accuracy· 2021-03-30
65.5
best: 84.3 (ViCE)
SOTA
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070
Semantic SegmentationonCityscapes test
mIoU· 2021-03-30
12.3
best: 67.3 (EDANet)
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070
Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2021-03-30
48.1
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070

Computer Vision3 results

Unsupervised Semantic SegmentationonCityscapes test
Accuracy· 2021-03-30
65.5
best: 84.3 (ViCE)
SOTA
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070
Unsupervised Semantic SegmentationonCityscapes test
mIoU· 2021-03-30
12.3
best: 26.8 (CUPS)
SOTA
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070
Unsupervised Semantic SegmentationonCOCO-Stuff-27
Clustering [Accuracy]· 2021-03-30
48.1
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070

Audio3 results

10-shot image generationonCityscapes test
Accuracy· 2021-03-30
65.5
best: 84.3 (ViCE)
SOTA
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070
10-shot image generationonCityscapes test
mIoU· 2021-03-30
12.3
best: 67.3 (EDANet)
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070
10-shot image generationonCOCO-Stuff-27
Clustering [Accuracy]· 2021-03-30
48.1
best: 81.1 (DynaSeg - FSF (ResNet-18 FPN))
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering arXiv:2103.17070