Paper Info
Title
Image-text dual neural network with decision strategy for small-sample image classification.
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.
Year
DOI
Venue
2019
10.1016/j.neucom.2018.02.099
Neurocomputing
Keywords
Field
DocType
Small-sample image classification,Few-shot,Ensemble learning,Deep convolutional neural network
LabelMe,Pattern recognition,Semantic information,Decision strategy,Artificial intelligence,Artificial neural network,Contextual image classification,Fuse (electrical),Ensemble learning,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
328
0925-2312
5
PageRank 
References 
Authors
0.46
13
7
Name
Order
Citations
PageRank
Fangyi Zhu150.46
Zhanyu Ma253955.74
Xiaoxu Li3255.88
Guang Chen4304.68
Jen-Tzung Chien5268.34
Jing-Hao Xue61510.05
Jun Guo7657.47