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A MBGD enhancement method for imbalance smoothing

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Abstract

This paper addresses foreground-foreground imbalance in object detection. Firstly, we introduce Mini-batch Stochastic Gradient Descent (MBGD) with YOLO and the foreground-foreground imbalance problem. Then T-distribution is devised and proved to smoothen the imbalanced distribution and allocate at least a representative for each class. Furthermore, Mini-Batch Imbalance Smoothing method (MB-IS) is proposed to address the foreground-foreground imbalance by following T-distribution and proportionally assigning class weights in a mini-batch. Finally, Extensive experiments on our own transaction dataset and VOC2007 dataset demonstrate the superiority of MB-IS with certain mini-batch size.

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China under grant No. 61702351, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant No. 17KJB520036, Foundation of Key Laboratory in Science and Technology Development Project of Suzhou under grant No. SZS201609, Suzhou Science and Technology Plan Project under Grant SYG201903, and Computer Basic Education Teaching Research Project under Grant 2018-AFCEC-328.

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Authors and Affiliations

  1. Software and Service Outsourcing College, Suzhou Vocational Institute of Industrial Technology, Suzhou, 215104, People’s Republic of China

    Xusheng Ai & Chunhua Li

  2. Department of Computer Science, Texas Tech University, Lubbock, TX, 79409, USA

    Victor S. Sheng

Authors
  1. Xusheng Ai
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  2. Victor S. Sheng
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  3. Chunhua Li
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Correspondence to Xusheng Ai.

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Conflict of Interests

This study was funded by Natural Science Foundation of China (grant number: 61876217, 62176175), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (grant number: 17KJB520036), and Foundation of Key Laboratory in Science and Technology Development Project of Suzhou (grant number: SZS201609), Suzhou Science and Technology Plan Project (grant number: SYG201903). The authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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Ai, X., Sheng, V.S. & Li, C. A MBGD enhancement method for imbalance smoothing. Multimed Tools Appl 81, 24225–24243 (2022). https://doi.org/10.1007/s11042-022-12697-3

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  • DOI: https://doi.org/10.1007/s11042-022-12697-3

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