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2018

Object-Level Representation Learning for Few-Shot Image Classification

Long, Liangqu, Wang, Wei, Wen, Jun et al.

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Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task.

In this paper, we propose to exploit an additional big dataset with different categories to improve the accuracy of few-shot learning over our target dataset.
Our approach is based on the observation that images can be decomposed into objects, which may appear in images from both the additional dataset and our target dataset.
We use the object-level relation learned from the additional dataset to infer the similarity of images in our target dataset with unseen categories.

Object-Level Representation Learning for Few-Shot Image Classification · Around

Built on

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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Adam: A method for stochastic optimization
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D. P. Kingma and J. Ba · 2014
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How transferable are features in deep neural networks?
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J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
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