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Foreground Feature Selection and Alignment for Adaptive Object Detection

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13019))

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Abstract

Recently, remarkable progress has been witnessed in adaptive object detection, which aims to mitigate the distributional shifts between source domain and target domain. Domain-adversarial learning methods align the features of different levels to minimize the domain discrepancy, which have been proven effective for adapting object detectors. Most domain adaptation methods align whole-image features. Therefore, foreground alignment may be interfered by the backgrounds. In this work, we propose Foreground Feature Alignment Framework (FFAF) that strengthens the foreground alignment. One of our key contributions is the Foreground Selection Module (FSM), which captures the foreground features that are crucial for object detection and helpful for subsequent feature alignment. Additionally, we align the foreground features by integrating multi-level domain classifiers. Multi-level Domain adaptation (MDA) can simultaneously bridge the domain gap at various representation levels. We evaluate our method with multiple experiments, whose results demonstrate that our method achieves significant improvements in different cross-domain object detection tasks.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61976231, Grant U1611461, Grant 61573387, and Grant 61172141, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515011869, and in part by the Science and Technology Program of Guangzhou under Grant 201803030029.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Zixuan Huang, Huicheng Zheng & Manwei Chen

  2. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China

    Zixuan Huang, Huicheng Zheng & Manwei Chen

  3. Guangdong Key Laboratory of Information Security Technology, Guangzhou, China

    Zixuan Huang, Huicheng Zheng & Manwei Chen

Authors
  1. Zixuan Huang
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  2. Huicheng Zheng
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  3. Manwei Chen
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Corresponding author

Correspondence to Huicheng Zheng .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Huang, Z., Zheng, H., Chen, M. (2021). Foreground Feature Selection and Alignment for Adaptive Object Detection. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-88004-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88003-3

  • Online ISBN: 978-3-030-88004-0

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