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

• URL: http://arxiv.org/abs/2204.13314v1
• Date: Thu, 28 Apr 2022 07:22:47 GMT
• Title: Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation
• Authors: Jianrong Zhang, Tianyi Wu, Chuanghao Ding, Hongwei Zhao and Guodong Guo
• Abstract summary: We propose a novel region-level contrastive and consistency learning framework (RC2L) 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. Based on the proposed region-level contrastive and consistency regularization, we develop a region-level contrastive and consistency learning framework (RC2L) for semi-supervised semantic segmentation.
• Score: 30.1884540364192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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