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6 May 2019 A few-shot learning framework for air vehicle detection by similarity embedding
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Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110691Q (2019) https://doi.org/10.1117/12.2524389
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China
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.
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Juan Chen, Yuchuan Liu, Yicong Liu, Shiying Wang, and Siyuan Chen "A few-shot learning framework for air vehicle detection by similarity embedding", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691Q (6 May 2019); https://doi.org/10.1117/12.2524389
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