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Deep Dual-Stream Network with Scale Context Selection Attention Module for Semantic Segmentation

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Abstract

The fusion of multi-scale features has been an effective method to get state-of-the-art performance in semantic segmentation. In this work, we concentrate on two tricky problems—the intra-class inconsistency and the blur on the localization of object boundaries and tackle them by combining two separate multi-scale context features respectively. Specifically, we propose a dual-stream structure with the scale context selection attention module to enhance the capabilities for multi-scale processing, where one stream collects global-scale context and the other captures local-scale information. Meanwhile, the embedded scale context selection attention module in each stream can adaptively focus on different scale context information to get optimal scale features. Based on our dual-stream structure with attention modules, our network can efficiently make use of multi-scale context to generate more comprehensive and powerful features. Our experiments show that our dual-stream network with scale context selection attention module achieves promising performance on the PASCAL VOC 2012 and PASCAL-Person-Part datasets.

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Notes

  1. https://github.com/YifuLiuL/DSCANet.

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Acknowledgements

This work was supported by the Opening Foundation of the State Key Laboratory (No. 2014KF06), and the National Science and Technology Major Project (No. 2013ZX03005013).

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Authors and Affiliations

  1. Institution of Information Science and Electrical Engineering, Zhejiang University, Hangzhou, 310037, Zhejiang, China

    Yifu Liu, Chenfeng Xu, Zhihong Chen, Chao Chen, Han Zhao & Xinyu Jin

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  1. Yifu Liu
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Liu, Y., Xu, C., Chen, Z. et al. Deep Dual-Stream Network with Scale Context Selection Attention Module for Semantic Segmentation. Neural Process Lett 51, 2281–2299 (2020). https://doi.org/10.1007/s11063-019-10148-z

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