Abstract
Nowadays, existing object detection methods based on deep learning usually need vast amounts of training data and cannot deal with unseen classes of objects well. In this paper, we propose a new framework that applies one-shot learning to object detection. During the training period, the network learns an ability from known object classes to compare the similarity of two image parts. For the image of a new category, selective search seeks proposals in the first step. Then the comparison based on traditional feature is used to screen out some inaccurate proposals. Next, our deep learning model can extract features and measure the similarity through feature fusion (which means concatenating the channels of two feature maps in this paper). After these steps, we can obtain a temporary result. Based on this result and some proposals related to it, we refine the proposals through the intersection. Then we conduct second-round detection with new proposals and improve the accuracy. Experiments on different datasets demonstrate that our method is effective and has a certain transferability.
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Na, S., Yan, R. (2019). A New Learning-Based One Shot Detection Framework for Natural Images. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_8
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