Few-Shot Classification with Feature mAP Reconstruction Networks
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 substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.
Cite
Text
Wertheimer et al. "Few-Shot Classification with Feature mAP Reconstruction Networks." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.00792Markdown
[Wertheimer et al. "Few-Shot Classification with Feature mAP Reconstruction Networks." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/wertheimer2021cvpr-fewshot/) doi:10.1109/CVPR46437.2021.00792BibTeX
@inproceedings{wertheimer2021cvpr-fewshot,
title = {{Few-Shot Classification with Feature mAP Reconstruction Networks}},
author = {Wertheimer, Davis and Tang, Luming and Hariharan, Bharath},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2021},
pages = {8012-8021},
doi = {10.1109/CVPR46437.2021.00792},
url = {https://mlanthology.org/cvpr/2021/wertheimer2021cvpr-fewshot/}
}