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Asian Conference on Computer Vision

ACCV 2020: Computer Vision – ACCV 2020 pp 143–154Cite as

COG: COnsistent Data AuGmentation for Object Perception

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12624))

Abstract

Recently, data augmentation techniques for training conv-nets emerge one after another, especially focusing on image classification. They’re always applied to object detection without further careful design. In this paper we propose COG, a general domain migration scheme for augmentation. Specifically, based on a particular augmentation, we first analyze its inherent inconsistency, and then adopt an adaptive strategy to rectify ground-truths of the augmented input images. Next, deep detection networks are trained on the rectified data to achieve better performance. Our extensive experiments show that our method COG’s performance is superior to its competitor on detection and instance segmentation tasks. In addition, the results manifest the robustness of COG when faced with hyper-parameter variations, etc.

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Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zewen He

  2. School of Computer and Control Engineering, University of Chinese Academy of Science, Beijing, China

    Zewen He & Dingqian Zhang

  3. Horizon Robotics, Beijing, China

    Rui Wu

Authors
  1. Zewen He
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  2. Rui Wu
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  3. Dingqian Zhang
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Corresponding author

Correspondence to Zewen He .

Editor information

Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Cite this paper

He, Z., Wu, R., Zhang, D. (2021). COG: COnsistent Data AuGmentation for Object Perception. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12624. Springer, Cham. https://doi.org/10.1007/978-3-030-69535-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-69535-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69534-7

  • Online ISBN: 978-3-030-69535-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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