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Image-text dual neural network with decision strategy for small-sample image classification

Zhu, F; Ma, Z; Li, X; Chen, G; Chien, JT; Xue, JH; Guo, J; (2019) Image-text dual neural network with decision strategy for small-sample image classification. Neurocomputing , 328 pp. 182-188. 10.1016/j.neucom.2018.02.099. Green open access

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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.

Type: Article
Title: Image-text dual neural network with decision strategy for small-sample image classification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neucom.2018.02.099
Publisher version: https://doi.org/10.1016/j.neucom.2018.02.099
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Small-sample image classification, Few-shot, Ensemble learning, Deep convolutional neural network
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/10059690
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