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Adaptive Feature Enhancement Network for Semantic Segmentation

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Neural Information Processing (ICONIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1332))

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Abstract

Semantic segmentation is a fundamental and challenging problem in computer vision. Recent studies attempt to integrate feature information of different depths to improve the performance of segmentation tasks, and a few of them enhance the features before fusion. However, which areas of the feature should be strengthened and how to strengthen are still inconclusive. Therefore, in this work we propose an Adaptive Feature Enhancement Module (AFEM) that utilizes high-level features to adaptively enhance the key areas of low-level features. Meanwhile, an Adaptive Feature Enhancement Network (AFENet) is designed with AFEM to combine all the enhanced features. The proposed method is validated on representative semantic segmentation datasets, Cityscapes and PASCAL VOC 2012. In particular, 79.5% mIoU on the Cityscapes testing set is achieved without using fine-val data, which is 1.1% higher than the baseline network and the model size is smaller. The code of AFENet is available at https://github.com/KTMomo/AFENet.

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Acknowledgments

This work was supported by Major Scientific and Technological Special Project of Guizhou Province (No. 20183002) and Sichuan Science and Technology Program (No. 2019YFG0535).

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Authors and Affiliations

  1. Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China

    Kuntao Cao

  2. Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Kuntao Cao, Xi Huang & Jie Shao

  3. Sichuan Artificial Intelligence Research Institute, Yibin, 644000, China

    Jie Shao

Authors
  1. Kuntao Cao
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  2. Xi Huang
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  3. Jie Shao
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Corresponding author

Correspondence to Jie Shao .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Cao, K., Huang, X., Shao, J. (2020). Adaptive Feature Enhancement Network for Semantic Segmentation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_34

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  • DOI: https://doi.org/10.1007/978-3-030-63820-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63819-1

  • Online ISBN: 978-3-030-63820-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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