Papers › Bootstrapping Semantic Segmentation with Regional Contrast
Bootstrapping Semantic Segmentation with Regional Contrast
Shikun Liu, Shuaifeng Zhi, Edward Johns, Andrew J. Davison
We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs semi-supervised or supervised pixel-level contrastive learning on a sparse set of hard negative pixels, with minimal additional memory footprint. ReCo is easy to implement, being built on top of off-the-shelf segmentation networks, and consistently improves performance in both semi-supervised and supervised semantic segmentation methods, achieving smoother segmentation boundaries and faster convergence. The strongest effect is in semi-supervised learning with very few labels. With ReCo, we achieve high-quality semantic segmentation models, requiring only 5 examples of each semantic class. Code is available at https://github.com/lorenmt/reco.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2104.04465")
Code
Syntology Ran 3 of 6 code samples harvested from 2 repositories linked to this paper; 3 have no recorded run. Of those that ran: 3 ran with no contract checked.
By repository: official repository: 2 samples from 1 repository, 2 ran; community (archive-listed): 4 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
6 samples harvested; 3 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 2 of the 6 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.
Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.
8dbedbf6c023b3d1 · report
e6de040ded42cdfa · report
c9097c70e7d42890 · report
86573b17e433a69b · report
2ad3bb5407d55287 · report
924e2af324b1fbd5 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Semantic Segmentation | Cityscapes 100 samples labeled | ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 60.28% | #6 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 100 samples labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 56.53% | #10 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 66.44% | #24 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 64.94% | #26 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 68.50% | #22 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 67.53% | #23 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 68.69% | #21 of 23 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 1% labeled | ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pre-trained) | Validation mIoU | 63.60% | #1 of 6 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 1% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pre-trained) | Validation mIoU | 63.16% | #2 of 6 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | ReCo | Validation mIoU | 71.00% | #31 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 72.14% | #1 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 66.41% | #6 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 73.66% | #1 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | Validation mIoU | 68.85% | #8 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections