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Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation
April 11, 2024, 4:45 a.m. | Hui Xiao, Yuting Hong, Li Dong, Diqun Yan, Jiayan Zhuang, Junjie Xiong, Dongtai Liang, Chengbin Peng
cs.CV updates on arXiv.org arxiv.org
Abstract: Semi-supervised semantic segmentation relieves the reliance on large-scale labeled data by leveraging unlabeled data. Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data. However, unreliable pseudo-labeling can undermine the semi-supervision processes. In this paper, we propose an algorithm called Multi-Level Label Correction (MLLC), which aims to use graph neural networks to capture structural relationships in Semantic-Level Graphs (SLGs) and Class-Level Graphs (CLGs) to rectify erroneous pseudo-labels. Specifically, SLGs represent semantic …
abstract algorithm arxiv cs.cv data exploit however labeling paper patterns processes reliance scale segmentation semantic semi-supervised supervision type undermine
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