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    Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild

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    IJCV_final_source_files.pdf
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Zhang, Yongqiang cc
    Bai, Yancheng
    Ding, Mingli
    Ghanem, Bernard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    VCC Analytics Research Group
    Date
    2020-02-18
    Online Publication Date
    2020-02-18
    Print Publication Date
    2020-06
    Embargo End Date
    2021-02-18
    Submitted Date
    2018-12-23
    Permanent link to this record
    http://hdl.handle.net/10754/661946
    
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    Abstract
    Object detection results have been rapidly improved over a short period of time with the development of deep convolutional neural networks. Although impressive results have been achieved on large/medium sized objects, the performance on small objects is far from satisfactory and one of remaining open challenges is detecting small object in unconstrained conditions (e.g. COCO and WIDER FACE benchmarks). The reason is that small objects usually lack sufficient detailed appearance information, which can distinguish them from the backgrounds or similar objects. To deal with the small object detection problem, in this paper, we propose an end-to-end multi-task generative adversarial network (MTGAN), which is a general framework. In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi-task network, which describes each inputted image patch with a real/fake score, object category scores, and bounding box regression offsets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training process. Extensive experiments on the challenging COCO and WIDER FACE datasets demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods by a large margin.
    Citation
    Zhang, Y., Bai, Y., Ding, M., & Ghanem, B. (2020). Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild. International Journal of Computer Vision. doi:10.1007/s11263-020-01301-6
    Sponsors
    The majority of this work was done when Yongqiang Zhang was a visiting Ph.D. student at King Abdullah University of Science and Technology (KAUST), and the others are continued at Harbin Institute of Technology (HIT). This work was supported by Natural Science Foundation of China, Grant No. 61603372.
    Publisher
    Springer Nature
    Journal
    International Journal of Computer Vision
    DOI
    10.1007/s11263-020-01301-6
    Additional Links
    http://link.springer.com/10.1007/s11263-020-01301-6
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11263-020-01301-6
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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