Image-text dual model for small-sample image classification

doi:10.1007/978-981-10-7302-1_46

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	Image-text dual model for small-sample image classification
	Zhu, Fangyi 2; Li, Xiaoxu 2,3; Ma, Zhanyu 2; Chen, Guang 2; Peng, Pai 4; Guo, Xiaowei 4; Chien, Jen-Tzung 1; Guo, Jun 2
	2017
会议日期	October 11, 2017 - October 14, 2017
会议地点	Tianjin, China
关键词	Classification (of information) Computer vision Deep neural networks Image enhancement Neural networks Sports Text processing Classification accuracy Classification performance Convolutional neural network Ensemble learning Image texts Small samples Text classification models Text information
卷号	772
DOI	10.1007/978-981-10-7302-1_46
页码	556-565
英文摘要	Small-sample classification is a challenging problem in computer vision and has many applications. In this paper, we propose an image-text dual model to improve the classification performance on small-sample dataset. The proposed dual model consists of two sub-models, an image classification model and a text classification model. After training the sub-models respectively, we design a novel method to fuse the two sub-models rather than simply combining the two models’ results. Our image-text dual model aims to utilize the text information to overcome the problem of training deep models on small-sample datasets. To demonstrate the effectiveness of the proposed dual model, we conduct extensive experiments on LabelMe and UIUC-Sports. Experimental results show that our model is superior to other models. In conclusion, our proposed model can achieve the highest image classification accuracy among all the referred models on LabelMe and UIUC-Sports. © Springer Nature Singapore Pte Ltd. 2017.
会议录	Communications in Computer and Information Science
会议录出版者	Springer Verlag
语种	英语
ISSN号	18650929
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/118154]
专题	兰州理工大学
作者单位	1.Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu City, Taiwan 2.Pattern Recognition and Intelligent System Lab, Beijing University of Posts and Telecommunications, Beijing, China; 3.School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China; 4.Youtu Lab, Tecent Technology, Shanghai, China;
推荐引用方式 GB/T 7714	Zhu, Fangyi,Li, Xiaoxu,Ma, Zhanyu,et al. Image-text dual model for small-sample image classification[C]. 见:. Tianjin, China. October 11, 2017 - October 14, 2017.