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arXiv · 2303.08029

Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation

Jianjian Yin·Zhichao Zheng·Yanhui Gu·Junsheng Zhou·Yi Chen
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Abstract

Existing approaches focus on using class-level features to improve semantic segmentation performance. How to characterize the relationships of intra-class pixels and inter-class pixels is the key to extract the discriminative representative class-level features. In this paper, we introduce for the first time to describe intra-class variations by multiple distributions. Then, multiple distributions representation learning(\textbf{MDRL}) is proposed to augment the pixel representations for semantic segmentation. Meanwhile, we design a class multiple distributions consistency strategy to construct discriminative multiple distribution representations of embedded pixels. Moreover, we put forward a multiple distribution semantic aggregation module to aggregate multiple distributions of the corresponding class to enhance pixel semantic information. Our approach can be seamlessly integrated into popular segmentation frameworks FCN/PSPNet/CCNet and achieve 5.61\%/1.75\%/0.75\% mIoU improvements on ADE20K. Extensive experiments on the Cityscapes, ADE20K datasets have proved that our method can bring significant performance improvement.

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Jianjian Yin, Zhichao Zheng, Yanhui Gu, Junsheng Zhou, Yi Chen. 2023-03-14. Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation. https://arxiv.org/abs/2303.08029

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