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A Hybrid Representation of Word Images for Keyword Spotting Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Feature Redirection Network for Few-Shot Classification

Authors : Yanan Wang, Guoqiang Zhong, Yuxu Mao, Kaizhu Huang

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Few-shot classification aims to learn novel categories by giving few labeled samples. How to make best use of the limited data to obtain a learner with fast learning ability has become a challenging problem. In this paper, we propose a feature redirection network (FRNet) for few-shot classification to make the features more discriminative. The proposed FRNet not only highlights relevant category features of support samples, but also learns how to generate task-relevant features of query samples. Experiments conducted on three datasets have demonstrate its superiority over the state-of-the-art methods.

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previous chapter Exploring Spatiotemporal Features for Activity Classifications in Films
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Metadata
Title
Feature Redirection Network for Few-Shot Classification
Authors
Yanan Wang
Guoqiang Zhong
Yuxu Mao
Kaizhu Huang
Copyright Year
2020
Book
Neural Information Processing

Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63820-7_48

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