# Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation

Source: [Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6c601e209131cea33ad0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:09:48.320Z

Authors: Jianrong Zhang, Tianyi Wu, Chuanghao Ding, Hongwei Zhao, Guodong Guo

Published: 2022-04-28T07:22:47.000Z

## Abstract

Current semi-supervised semantic segmentation methods mainly focus on designing pixel-level consistency and contrastive regularization. However, pixel-level regularization is sensitive to noise from pixels with incorrect predictions, and pixel-level contrastive regularization has memory and computational cost with O(pixel\_num^2). To address the issues, we propose a novel region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation. Specifically, we first propose a Region Mask Contrastive (RMC) loss and a Region Feature Contrastive (RFC) loss to accomplish region-level contrastive property. Furthermore, Region Class Consistency (RCC) loss and Semantic Mask Consistency (SMC) loss are proposed for achieving region-level consistency. Based on the proposed region-level contrastive and consistency regularization, we develop a region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation, and evaluate our RC$^2$L on two challenging benchmarks (PASCAL VOC 2012 and Cityscapes), outperforming the state-of-the-art.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on Pascal VOC 2012 (val) (Resolution-Dependent mIoU)](<https://www.sota2.com/research/sota/semantic-segmentation-on-pascal-voc-2012-val-resolution-dependent-miou.md>): Semantic segmentation; PASCAL VOC 2012 (val); mIoU: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.13314>)

[PDF](<https://arxiv.org/pdf/2204.13314.pdf>)