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dc.contributor.authorZhang, Yongqiang
dc.contributor.authorBai, Yancheng
dc.contributor.authorDing, Mingli
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2020-03-09T05:54:36Z
dc.date.available2020-03-09T05:54:36Z
dc.date.issued2020-02-18
dc.date.submitted2018-12-23
dc.identifier.citationZhang, 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
dc.identifier.doi10.1007/s11263-020-01301-6
dc.identifier.urihttp://hdl.handle.net/10754/661946
dc.description.abstractObject 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.
dc.description.sponsorshipThe 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.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/10.1007/s11263-020-01301-6
dc.rightsArchived with thanks to International Journal of Computer Vision
dc.titleMulti-task Generative Adversarial Network for Detecting Small Objects in the Wild
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVCC Analytics Research Group
dc.identifier.journalInternational Journal of Computer Vision
dc.rights.embargodate2021-02-18
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
dc.contributor.institutionInstitute of Software, Chinese Academy of Sciences, Beijing, China
kaust.personGhanem, Bernard
dc.date.accepted2020-02-04
refterms.dateFOA2021-02-18T00:00:00Z
dc.date.published-online2020-02-18
dc.date.published-print2020-06


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