paper-with-me

Semi-Supervised Semantic Segmentation 벤치마크

Semi-Supervised Semantic Segmentation on PASCAL Context 25% labeled

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Validation mIoU

37.8 38.77 39.75 40.73 41.7 2019-08 2026-09 s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) — 37.8 (2019-08-15) s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) — 37.8 (2019-08-15) GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) — 41.7 (2021-06-29) GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) — 41.7 (2021-06-29) s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) — 37.8 (2019-08-15) GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) — 41.7 (2021-06-29)
RankModel Validation mIoU PaperCodeYear
1 GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) 41.7% GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference yh-pengtu/GuidedMix-Net 2021
2 s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) 37.8 Semi-Supervised Semantic Segmentation with High- and Low-level Consistency sud0301/semisup-semseg 2019
3 GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) 41.7% GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference yh-pengtu/GuidedMix-Net 2021
4 s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) 37.8 Semi-Supervised Semantic Segmentation with High- and Low-level Consistency sud0301/semisup-semseg 2019
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