Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
- URL: http://arxiv.org/abs/2108.09025v1
- Date: Fri, 20 Aug 2021 07:04:33 GMT
- Title: Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
- Authors: Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, Yu-Xiong
Wang
- Abstract summary: We present a novel semi-supervised semantic segmentation method which jointly achieves two desiderata of segmentation model regularities.
We leverage the pixel-level L2 loss and the pixel contrastive loss for the two purposes respectively.
- Score: 22.920856071095915
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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