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Proceedings Paper

A few-shot learning framework for air vehicle detection by similarity embedding
Author(s): Juan Chen; Yuchuan Liu; Yicong Liu; Shiying Wang; Siyuan Chen
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Paper Abstract

Air vehicles such as aircrafts and drones have played an important role in surveillance and transportation for both civil and military applications. In this paper, we proposed a few-shot learning framework for air vehicle detection by similarity embedding, with a single moving camera mounted on another flying object. Firstly, we presented the example embedding with similarity conditioned LSTM-model for air vehicle detection. Secondly, we described the support set embedding with bidirectional LSTM-model of air vehicle training samples. Thirdly, we introduced the label prediction for air vehicle image blocks by attention kernel. Finally, we applied the fully convolutional network to segment air vehicle in the accurate bounding box. Experiment results of air vehicle detection show the effectiveness of our approach.

Paper Details

Date Published: 6 May 2019
PDF: 5 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691Q (6 May 2019); doi: 10.1117/12.2524389
Show Author Affiliations
Juan Chen, Univ. of Electronic Science and Technology of China (China)
Yuchuan Liu, Univ. of Electronic Science and Technology of China (China)
Yicong Liu, Southwest Automation Research Institute (China)
Shiying Wang, Univ. of Electronic Science and Technology of China (China)
Siyuan Chen, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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