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Semi-Supervised Semantic Segmentation archive 2025-07-28

Cityscapes 12.5% labeled Benchmark (Semi-Supervised Semantic Segmentation)

33 rows 31 with code listed 1 metric Dataset page

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).

The archive carries no text for this table; the description above is the archive's text for the task Semi-Supervised Semantic Segmentation. archive 2025-07-28

Over time archive 2025-07-28

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Not inferred: Validation mIoU. Points are placed at the row's paper date; 33 of 33 rows carry one.

Results archive 2025-07-28

Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.

Paper Code Ran Syntology Report
1 UniMatch V2 (DINOv2-B) 84.3% – Paper Code 2024 2 of 5 ran · 3 unverified report
2 SemiVL (ViT-B/16) 79.4% – Paper Code 2023 linked, not harvested report
3 PrevMatch (ResNet-101) 78.9% – Paper Code 2024 6 of 11 ran · 5 unverified report
4 CorrMatch (Deeplabv3+ with ResNet-101) 78.5% – Paper Code 2023 linked, not harvested report
5 FARCLUSS 78.5 – Paper Code 2025 linked, not harvested report
6 Dual Teacher 78.4 – Paper Code 2023 linked, not harvested report
7 UniMatch (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) 77.92% – Paper Code 2022 4 of 9 ran · 5 unverified report
8 AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) 77.9% – Paper Code 2021 1 of 1 ran · 0 unverified report
9 PrevMatch (ResNet-50) 77.8% – Paper Code 2024 6 of 11 ran · 5 unverified report
10 S4MC 77.78% – Paper Code 2023 linked, not harvested report
11 CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) 77.62% – Paper Code 2021 22 of 39 ran · 17 unverified report
12 n-CPS (ResNet-50) 77.61% – Paper – 2021 no code linked report
13 CW-BASS (DeepLab v3+ with ResNet-50) 77.20% – Paper Code 2025 linked, not harvested report
14 PS-MT (DeepLab v3+ with ImageNet-pretrained ResNet50, single scale inference) 77.12% – Paper Code 2021 linked, not harvested report
15 LaserMix (DeepLab v3+, ImageNet pre-trained ResNet50, single scale inference) 77.1% – Paper Code 2022 linked, not harvested report
16 U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL) 76.48% – Paper Code 2022 1 of 1 ran · 0 unverified report
17 PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) 76.31% – Paper Code 2022 linked, not harvested report
18 CPCL (DeepLab v3+ with ResNet-50) 74.6% – Paper Code 2022 linked, not harvested report
19 SimpleBaseline(DeeplabV3+ with ImageNet pretrained Xception65, sinle scale inference) 74.1% – Paper Code 2021 linked, not harvested report
20 Dense FixMatch (DeepLabv3+ ResNet-101, uniform sampling, single pass eval) 73.91% – Paper Code 2022 linked, not harvested report
21 Dense FixMatch (DeepLabv3+ ResNet-50, uniform sampling, single pass eval) 73.39% – Paper Code 2022 linked, not harvested report
22 Error Localization Network (DeeplabV3 with ResNet-50) 70.33% – Paper Code 2022 1 of 7 ran · 6 unverified report
23 SegSDE (MTL decoder with ResNet101, ImageNet pretrained, unlabeled image sequences) 68.01% ✓ Paper Code 2020 linked, not harvested report
24 ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained) 66.44% – Paper Code 2021 3 of 6 ran · 3 unverified report
25 GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) 65.8% – Paper Code 2021 linked, not harvested report
26 ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) 64.94% – Paper Code 2021 3 of 6 ran · 3 unverified report
27 SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained) 64.4% – Paper Code 2021 1 of 4 ran · 3 unverified report
28 DMT (DeepLab v2 MSCOCO/ImageNet pre-trained) 63.03% – Paper Code 2020 1 of 1 ran · 0 unverified report
29 GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) 62.57% – Paper – 2021 no code linked report
30 ClassMix (DeepLab v2 MSCOCO pretrained) 61.35% – Paper Code 2020 5 of 19 ran · 14 unverified report
31 CutMix (DeepLab v2, ImageNet pre-trained) 60.34% – Paper Code 2019 0 of 3 ran · 3 unverified report
32 s4GAN (DeepLab v2 ImageNet pre-trained) 59.3% – Paper Code 2019 4 of 4 ran · 0 unverified report
33 Adversarial (DeepLab v2 ImageNet pre-trained) 57.1% – Paper Code 2018 1 of 1 ran · 0 unverified report

All 33 rows shown. 33 link to a paper page on this site; 1 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28

Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 16 rows have a graph line, from 14 distinct papers; 15 rows (13 papers) have at least one sample that ran. Counting each paper once: Syntology ran 52 of 111 samples; 59 unverified. Separately, 35 of those 111 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.

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