Title: Image-text dual neural network with decision strategy for small-sample image classification
Authors: Zhu, Fangyi
Ma, Zhanyu
Li, Xiaoxu
Chen, Guang
Chien, Jen-Tzung
Xue, Jing-Hao
Guo, Jun
電機工程學系
Department of Electrical and Computer Engineering
Keywords: Small-sample image classification;Few-shot;Ensemble learning;Deep convolutional neural network
Issue Date: 7-Feb-2019
Abstract: Small-sample classification is a challenging problem in computer vision. In this work, we show how to efficiently and effectively utilize semantic information of the annotations to improve the performance of small-sample classification. First, we propose an image-text dual neural network to improve the classification performance on small-sample datasets. The proposed model consists of two sub-models, an image classification model and a text classification model. After training the sub-models separately, we design a novel method to fuse the two sub-models rather than simply combine their results. Our image-text dual neural network aims to utilize the text information to overcome the training problem of deep models on small-sample datasets. Then, we propose to incorporate a decision strategy into the image-text dual neural network to further improve the performance of our original model on few-shot datasets. To demonstrate the effectiveness of the proposed models, we conduct experiments on the LabelMe and UIUC-Sports datasets. Experimental results show that our method is superior to other models. (C) 2018 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.neucom.2018.02.099
http://hdl.handle.net/11536/148837
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.02.099
Journal: NEUROCOMPUTING
Volume: 328
Begin Page: 182
End Page: 188
Appears in Collections:Articles