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Robustizing Object Detection Networks Using Augmented Feature Pooling

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13845))

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Abstract

This paper presents a framework to robustize object detection networks against large geometric transformation. Deep neural networks rapidly and dramatically have improved object detection performance. Nevertheless, modern detection algorithms are still sensitive to large geometric transformation. Aiming at improving the robustness of the modern detection algorithms against the large geometric transformation, we propose a new feature extraction called augmented feature pooling. The key is to integrate the augmented feature maps obtained from the transformed images before feeding it to the detection head without changing the original network architecture. In this paper, we focus on rotation as a simple-yet-influential case of geometric transformation, while our framework is applicable to any geometric transformations. It is noteworthy that, with only adding a few lines of code from the original implementation of the modern object detection algorithms and applying simple fine-tuning, we can improve the rotation robustness of these original detection algorithms while inheriting modern network architectures’ strengths. Our framework overwhelmingly outperforms typical geometric data augmentation and its variants used to improve robustness against appearance changes due to rotation. We construct a dataset based on MS COCO to evaluate the robustness of the rotation, called COCO-Rot. Extensive experiments on three datasets, including our COCO-Rot, demonstrate that our method can improve the rotation robustness of state-of-the-art algorithms.

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Notes

  1. 1.

    Our code of will be available at http://www.ok.sc.e.titech.ac.jp/res/DL/index.html.

  2. 2.

    Note that the TTA curve assumes that each inference before ensemble is ideal, and thus this occupancy is the upper bound.

  3. 3.

    The dimensions of feature map \({{\mathbf{{x}}}}^{l}\) are the same as the original backbones.

  4. 4.

    The details of our dataset are described in our supplemental.

  5. 5.

    As shown in our supplemental, AP\(_{50}\) and AP\(_{75}\) are also the highest in the proposed method as well as mAP.

  6. 6.

    Note that, in PASCAL VOC, the standard evaluation metric is AP\(_{50}\).

  7. 7.

    We also show AP\(_{50}\) and AP\(_{75}\) in our supplementary material.

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Authors and Affiliations

  1. NTT Corporation, Kanagawa, Japan

    Takashi Shibata

  2. Tokyo Institute of Technology, Tokyo, Japan

    Masayuki Tanaka & Masatoshi Okutomi

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  1. Takashi Shibata
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  2. Masayuki Tanaka
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  3. Masatoshi Okutomi
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Correspondence to Takashi Shibata .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Shibata, T., Tanaka, M., Okutomi, M. (2023). Robustizing Object Detection Networks Using Augmented Feature Pooling. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13845. Springer, Cham. https://doi.org/10.1007/978-3-031-26348-4_6

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  • DOI: https://doi.org/10.1007/978-3-031-26348-4_6

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