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Salient feature network for semantic segmentation

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Abstract

In the encoding stage, some existing semantic segmentation networks capture rich multi-scale context information. However, multi-scale approaches do not pay attention to the correlation between different scale feature maps in the multi-scale feature fusion stage. In the decoding stage, simple fusion of high- and low-dimensional channel is used to improve the semantic segmentation, but simple fusion suffers from the defect that the segmentation boundary is not sufficiently clear. In this paper, a salient feature network is proposed to address these two disadvantages. For the first shortcoming, an atrous spatial pyramid pooling with Euclidean distance similarity (EDS-ASPP) module is proposed to enhance the representation of high-level semantic information features, that is, to boost meaningful features, while suppressing weak ones. Therefore, this module can solve the segmentation error inside objects. For the second deficiency, a supplementary details (SD) module is proposed to rearrange the low-level spatial details and the activation graph obtained from the EDS-ASPP module in the decoding stage. The function of this module is to repair the edge details lost during the downsampling process. The proposed model achieves a 73.45% mIoU on PASCAL VOC2012 and a 64.27% mIoU on Cityscapes.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61773330), the National key R&D Program of China (SQ2020YFA070205), the Project of Shanghai Municipal Science and Technology Commission (19511120900), the Research Project of the Department of Education of Hunan Province (19C1740), and Xiangtan University Innovation Foundation for Postgraduate (XDCX2020B083).

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Authors and Affiliations

  1. School of Automation and Electronic Information, Xiangtan University, Xiangtan, 411105, China

    Yan Zhou, Zhen Zhou & Dongli Wang

  2. School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China

    Haibin Zhou

  3. Shanghai Aerospace Control Technology Institute, Shanghai, 201109, China

    Jinzhen Mu

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  1. Yan Zhou
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  2. Zhen Zhou
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  3. Haibin Zhou
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Correspondence to Yan Zhou.

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Zhou, Y., Zhou, Z., Zhou, H. et al. Salient feature network for semantic segmentation. SIViP 16, 763–771 (2022). https://doi.org/10.1007/s11760-021-02016-y

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