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Fine-Grained Few-Shot Classification with Feature Map Reconstruction Networks

2020-12-02 20:19:09
Davis Wertheimer, Luming Tang, Bharath Hariharan
arXiv_CV
       
Abstract

In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and significant accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet benchmark with minimal bells and whistles.

Abstract (translated)

URL

https://arxiv.org/abs/2012.01506

PDF

https://arxiv.org/pdf/2012.01506.pdf

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