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 |