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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Acknowledgments
This work was supported by the Major Project for New Generation of AI under Grant No. 2018AAA0100400, the National Natural Science Foundation of China (NSFC) under Grants No. 41706010 and No. 61876155, the Joint Fund of the Equipments Pre-Research and Ministry of Education of China under Grant No. 6141A020337, the Natural Science Foundation of Jiangsu Province under Grant No. BK20181189, the Key Program Special Fund in XJTLU under Grants No. KSF-A-01, KSF-T-06, KSF-E-26, KSF-P-02 and KSF-A-10, and the Fundamental Research Funds for the Central Universities of China.
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Wang, Y., Zhong, G., Mao, Y., Huang, K. (2020). Feature Redirection Network for Few-Shot Classification. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_48
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DOI: https://doi.org/10.1007/978-3-030-63820-7_48
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