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Multi-scale Attention-Based Feature Pyramid Networks for Object Detection

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12888))

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Abstract

Feature pyramid network (FPN) is widely used for multi-scale object detection. While lots of FPN based methods have been proposed to improve detection performance, there exists semantic difference between cross-scale features. Therefore, simple connections bring spatial or channel information loss, while excessive connections bring extra parameters and inference cost. Besides, the fusion of too many features may lead to the information decay and feature aliasing. To deal with the above problems, we propose the Multi-scale Attention-based Feature Pyramid Networks (MAFPN), to fully exploit spatial and channel information and generate a better feature representation for each level from multi-scale features. Taking scale, spatial and channel information into consideration at the same time, MAFPN can process multi-scale input more comprehensively than most conventional methods. The experimental results show that our MAFPN can improve the detection performance of both two-stage and one-stage detectors with an acceptable increase of inference cost.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 91959108

Author information

Authors and Affiliations

  1. Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Xiaodong Zhao, Junliang Chen, Minmin Liu, Kai Ye & Linlin Shen

  2. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China

    Xiaodong Zhao, Junliang Chen, Minmin Liu, Kai Ye & Linlin Shen

Authors
  1. Xiaodong Zhao
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  2. Junliang Chen
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  3. Minmin Liu
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  4. Kai Ye
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  5. Linlin Shen
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Corresponding author

Correspondence to Linlin Shen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Zhao, X., Chen, J., Liu, M., Ye, K., Shen, L. (2021). Multi-scale Attention-Based Feature Pyramid Networks for Object Detection. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_34

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  • DOI: https://doi.org/10.1007/978-3-030-87355-4_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87354-7

  • Online ISBN: 978-3-030-87355-4

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