@article{8849cb586f4e4f9f84264533fbcb3a04,
title = "A MBGD enhancement method for imbalance smoothing",
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.",
keywords = "Foreground-foreground imbalance, Mini-batch stochastic gradient descent, Object detection, YOLO",
author = "Xusheng Ai and Sheng, {Victor S.} and Chunhua Li",
note = "Funding Information: 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. Funding Information: 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. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2022",
doi = "10.1007/s11042-022-12697-3",
language = "English",
journal = "Multimedia Tools and Applications",
issn = "1380-7501",
}