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Models/URN(ResNet-101, no saliency, no RW)

URN(ResNet-101, no saliency, no RW)

Reported on 6 benchmarks across 2 tasks · 1 paper

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

Medical3 results

Semantic SegmentationonCOCO 2014 val
mIoU· 2021-12-14
40.7
best: 56.8 (DHR (Swin-L, Mask2Former))
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation arXiv:2112.07431
Semantic SegmentationonPASCAL VOC 2012 val
Mean IoU· 2021-12-14
69.5
best: 83.4 (SemPLeS (Swin-L))
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation arXiv:2112.07431
Semantic SegmentationonPASCAL VOC 2012 test
Mean IoU· 2021-12-14
69.7
best: 82.9 (SemPLeS (Swin-L))
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation arXiv:2112.07431

Audio3 results

10-shot image generationonCOCO 2014 val
mIoU· 2021-12-14
40.7
best: 56.8 (DHR (Swin-L, Mask2Former))
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation arXiv:2112.07431
10-shot image generationonPASCAL VOC 2012 val
Mean IoU· 2021-12-14
69.5
best: 83.4 (SemPLeS (Swin-L))
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation arXiv:2112.07431
10-shot image generationonPASCAL VOC 2012 test
Mean IoU· 2021-12-14
69.7
best: 82.9 (SemPLeS (Swin-L))
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation arXiv:2112.07431