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RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13669))

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Abstract

Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on tiny object detection tasks. In this paper, we point out that either box prior in the anchor-based detector or point prior in the anchor-free detector is sub-optimal for tiny objects. Our key observation is that the current anchor-based or anchor-free label assignment paradigms will incur many outlier tiny-sized ground truth samples, leading to detectors imposing less focus on the tiny objects. To this end, we propose a Gaussian Receptive Field based Label Assignment (RFLA) strategy for tiny object detection. Specifically, RFLA first utilizes the prior information that the feature receptive field follows Gaussian distribution. Then, instead of assigning samples with IoU or center sampling strategy, a new Receptive Field Distance (RFD) is proposed to directly measure the similarity between the Gaussian receptive field and ground truth. Considering that the IoU-threshold based and center sampling strategy are skewed to large objects, we further design a Hierarchical Label Assignment (HLA) module based on RFD to achieve balanced learning for tiny objects. Extensive experiments on four datasets demonstrate the effectiveness of the proposed methods. Especially, our approach outperforms the state-of-the-art competitors with 4.0 AP points on the AI-TOD dataset. Codes are available at https://github.com/Chasel-Tsui/mmdet-rfla.

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Acknowledgement

This work was partly supported by the Fundamental Research Funds for the Central Universities under Grant 2042022kf1010, and the National Natural Science Foundation of China under Grant 61771351 and 61871297. The numerical calculations were conducted on the supercomputing system in the Supercomputing Center, Wuhan University.

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, China

    Chang Xu, Wen Yang, Huai Yu & Lei Yu

  2. Huawei Technologies Co., Ltd., Shenzhen, China

    Jinwang Wang

  3. School of Computer Science, Wuhan University, Wuhan, China

    Gui-Song Xia

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  1. Chang Xu
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  2. Jinwang Wang
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  3. Wen Yang
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  4. Huai Yu
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  5. Lei Yu
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  6. Gui-Song Xia
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Correspondence to Wen Yang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Xu, C., Wang, J., Yang, W., Yu, H., Yu, L., Xia, GS. (2022). RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_31

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  • DOI: https://doi.org/10.1007/978-3-031-20077-9_31

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