Computer Science > Computer Vision and Pattern Recognition
arXiv:2210.02476 (cs)
[Submitted on 5 Oct 2022]
Title:BaseTransformers: Attention over base data-points for One Shot Learning
View a PDF of the paper titled BaseTransformers: Attention over base data-points for One Shot Learning, by Mayug Maniparambil and 2 other authors
View PDFAbstract:Few shot classification aims to learn to recognize novel categories using only limited samples per category. Most current few shot methods use a base dataset rich in labeled examples to train an encoder that is used for obtaining representations of support instances for novel classes. Since the test instances are from a distribution different to the base distribution, their feature representations are of poor quality, degrading performance. In this paper we propose to make use of the well-trained feature representations of the base dataset that are closest to each support instance to improve its representation during meta-test time. To this end, we propose BaseTransformers, that attends to the most relevant regions of the base dataset feature space and improves support instance representations. Experiments on three benchmark data sets show that our method works well for several backbones and achieves state-of-the-art results in the inductive one shot setting. Code is available atthis http URL
| Comments: | Paper accepted at British Machine Vision Conference 2022 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2210.02476 [cs.CV] |
| (orarXiv:2210.02476v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2210.02476 arXiv-issued DOI via DataCite |
Submission history
From: Mayug Maniparambil [view email][v1] Wed, 5 Oct 2022 18:00:24 UTC (12,975 KB)
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View a PDF of the paper titled BaseTransformers: Attention over base data-points for One Shot Learning, by Mayug Maniparambil and 2 other authors
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