Papers › Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

16 Mar 2022ICLR 2022 4arXiv:2203.08414archive 2025-07-28

Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman

Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce features for every pixel that are both semantically meaningful and compact enough to form distinct clusters. Unlike previous works which achieve this with a single end-to-end framework, we propose to separate feature learning from cluster compactification. Empirically, we show that current unsupervised feature learning frameworks already generate dense features whose correlations are semantically consistent. This observation motivates us to design STEGO (Self-supervised Transformer with Energy-based Graph Optimization), a novel framework that distills unsupervised features into high-quality discrete semantic labels. At the core of STEGO is a novel contrastive loss function that encourages features to form compact clusters while preserving their relationships across the corpora. STEGO yields a significant improvement over the prior state of the art, on both the CocoStuff (+14 mIoU) and Cityscapes (+9 mIoU) semantic segmentation challenges.

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mhamilton723/STEGO officialmentioned on GitHubpytorch report
leggedrobotics/self_supervised_segmentation mentioned on GitHubpytorchMIT report
merantix-momentum/stego-studies mentioned on GitHubpytorch report

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2ran · honoured contract
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one_hot_feats mhamilton723/STEGO/src/train_segmentation.py official repository ran · our draft was wrong MIT (permissive) · eb2b27a0c094426d · report
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Tasks

FormSemantic SegmentationUnsupervised Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Semantic Segmentation COCO-Stuff-27 STEGO (ViT-B/8) Clustering [Accuracy] 56.9 #8 of 29 Archive leaderboard report
Unsupervised Semantic Segmentation COCO-Stuff-27 STEGO (ViT-B/8) Clustering [mIoU] 28.2 #8 of 29 Archive leaderboard report
Unsupervised Semantic Segmentation COCO-Stuff-27 STEGO (ViT-S/8) Clustering [mIoU] 24.5 #17 of 29 Archive leaderboard report
Unsupervised Semantic Segmentation COCO-Stuff-27 STEGO (ViT-S/8) Linear Classifier [Accuracy] 74.4 #17 of 29 Archive leaderboard report
Unsupervised Semantic Segmentation COCO-Stuff-27 STEGO (ViT-S/8) Linear Classifier [mIoU] 38.3 #17 of 29 Archive leaderboard report
Unsupervised Semantic Segmentation Cityscapes test STEGO Accuracy 73.2 #6 of 14 Archive leaderboard report
Unsupervised Semantic Segmentation Cityscapes test STEGO mIoU 21.0 #6 of 14 Archive leaderboard report
Unsupervised Semantic Segmentation Potsdam-3 STEGO Accuracy 77.0 #6 of 8 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

AdamAttentionCRFDINODropoutLayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSoftmaxTransformerVision Transformer

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