Dynamic Multi-view Combination for Image Classification
Li,Chenghua1; Wang,Wanguo2; Liu,Fengyuan1,3; Guo,Zhenyu1,3
Source PublicationJournal of Physics: Conference Series
AbstractAbstract Multi-view learning is widely used in image classification tasks to better explore the discriminative information of different views. However, existing multi-view methods commonly rely on some pre-defined assumptions or fail to fully take advantage of the combination commonality between individual images. This paper presents an efficient dynamic multi-view combination approach to dynamically combine the discriminative power of different views. Specially, we firstly utilize a group of pre-trained CNNs to extract different views of an image. Secondly, we apply a dynamic gating module to the image, which will generate a weight vector of these views to model the image-level information for the multi-view learning. Finally, the weight vector and the views are combined for the classification. Experimental results and analysis on CIFAR-10 and ImageNet show the effectiveness of the proposed dynamic multi-view combination method (DMVC) for visual classification.
WOS IDIOP:1742-6588-1631-1-012125
PublisherIOP Publishing
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Document Type期刊论文
Affiliation1.NLPR & AIRIA, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd., Beijing 100190, China
2.State Grid Intelligence Technology Co., Ltd., China
3.University of Chinese Academy of Sciences, Beijing 100190, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li,Chenghua,Wang,Wanguo,Liu,Fengyuan,et al. Dynamic Multi-view Combination for Image Classification[J]. Journal of Physics: Conference Series,2020,1631(1).
APA Li,Chenghua,Wang,Wanguo,Liu,Fengyuan,&Guo,Zhenyu.(2020).Dynamic Multi-view Combination for Image Classification.Journal of Physics: Conference Series,1631(1).
MLA Li,Chenghua,et al."Dynamic Multi-view Combination for Image Classification".Journal of Physics: Conference Series 1631.1(2020).
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