Papers › Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation

Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation

20 Aug 2021ICCV 2021 10arXiv:2108.09025archive 2025-07-28

Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, Yu-Xiong Wang

We present a novel semi-supervised semantic segmentation method which jointly achieves two desiderata of segmentation model regularities: the label-space consistency property between image augmentations and the feature-space contrastive property among different pixels. We leverage the pixel-level L2 loss and the pixel contrastive loss for the two purposes respectively. To address the computational efficiency issue and the false negative noise issue involved in the pixel contrastive loss, we further introduce and investigate several negative sampling techniques. Extensive experiments demonstrate the state-of-the-art performance of our method (PC2Seg) with the DeepLab-v3+ architecture, in several challenging semi-supervised settings derived from the VOC, Cityscapes, and COCO datasets.

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Tasks

Computational EfficiencySegmentationSemantic SegmentationSemi-Supervised Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semi-Supervised Semantic Segmentation COCO 1/128 labeled PC2Seg Validation mIoU 40.1 #8 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation COCO 1/256 labeled PC2Seg Validation mIoU 37.5 #8 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation COCO 1/32 labeled PC2Seg Validation mIoU 46.1 #6 of 7 Archive leaderboard report
Semi-Supervised Semantic Segmentation COCO 1/512 labeled PC2Seg Validation mIoU 29.9 #7 of 8 Archive leaderboard report
Semi-Supervised Semantic Segmentation COCO 1/64 labeled PC2Seg Validation mIoU 43.7 #8 of 9 Archive leaderboard report

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