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Class-Imbalance Aware CNN Extension for High Resolution Aerial Image based Vehicle Localization and Categorization
Li FM(李非墨)1,2; Li SX(李书晓)1; Zhu CF(朱承飞)1; Lan XS(兰晓松)1,2; Chang HX(常红星)1
2017-12
会议名称2017 2nd International Conference on Image, Vision and Computing
会议日期2017-6
会议地点中国四川成都
摘要High resolution aerial image based vehicle localization and categorization methods are crucial for many real life applications. Convolutional neural network based classifiers have already achieved very high performances, but are still suffering from the problem of class imbalance. To address this issue, an efficient bi-parted style network extension scheme based on a class-imbalance aware loss function is proposed.  This novel loss function is devised by adding an extra class-imbalance aware regularization term to the normal softmax loss, and will force the feature maps in the extended network structure to be more sensitive to samples from the minority classes. This network extension is compared with its strong equivalent counter-parts in experiment, and comparably significant improvements on the minority classes can be observed.
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14582
专题综合信息系统研究中心
作者单位1.中国科学院自动化所
2.中国科学院大学
推荐引用方式
GB/T 7714
Li FM,Li SX,Zhu CF,et al. Class-Imbalance Aware CNN Extension for High Resolution Aerial Image based Vehicle Localization and Categorization[C],2017.
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