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DOOBNet: Deep Object Occlusion Boundary Detection from an Image

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

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Abstract

Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. Solving this problem is challenging as we encounter extreme boundary/non-boundary class imbalance during the training of an object occlusion boundary detector. In this paper, we propose to address this class imbalance by up-weighting the loss contribution of false negative and false positive examples with our novel Attention Loss function. We also propose a unified end-to-end multi-task deep object occlusion boundary detection network (DOOBNet) by sharing convolutional features to simultaneously predict object boundary and occlusion orientation. DOOBNet adopts an encoder-decoder structure with skip connection in order to automatically learn multi-scale and multi-level features. We significantly surpass the state-of-the-art on the PIOD dataset (ODS F-score of .702) and the BSDS ownership dataset (ODS F-score of .555), as well as improving the detecting speed to as 0.037 s per image on the PIOD dataset.

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Notes

  1. 1.

    The statistics come from PIOD dataset.

  2. 2.

    The conv block refers to convolution layer followed by batch normalization (BN) [14] and ReLU activation.

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Acknowledgement

This work is supported by National Key R&D Program of China (2017YFB1002702) and National Nature Science Foundation of China (61572058). We would like to thank Peng Wang for helping with generating DOC experimental results and valuable discussions.

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Authors and Affiliations

  1. State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Guoxia Wang, Xiaochuan Wang & Xiaohui Liang

  2. Department of Computer Science, University of Durham, Durham, UK

    Frederick W. B. Li

Authors
  1. Guoxia Wang
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  2. Xiaochuan Wang
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  3. Frederick W. B. Li
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  4. Xiaohui Liang
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Corresponding author

Correspondence to Xiaohui Liang .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Wang, G., Wang, X., Li, F.W.B., Liang, X. (2019). DOOBNet: Deep Object Occlusion Boundary Detection from an Image. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_43

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  • DOI: https://doi.org/10.1007/978-3-030-20876-9_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20875-2

  • Online ISBN: 978-3-030-20876-9

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