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Abstract
In this paper, we look at the problem of few-shot image classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods use various adaptation strategies for aligning their visual representations to new domains or select the relevant ones from multiple domain-specific feature extractors. In this work, we present URL, which learns a single set of universal visual representations by distilling knowledge of multiple domain-specific networks after co-aligning their features with the help of adapters and centered kernel alignment. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step in a similar spirit to distance learning methods. We rigorously evaluate our model in the recent Meta-Dataset benchmark and demonstrate that it significantly outperforms the previous methods while being more efficient
Original language | English |
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Title of host publication | Proceedings of 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 9506-9515 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-6654-2812-5 |
ISBN (Print) | 978-1-6654-2813-2 |
DOIs | |
Publication status | Published - 28 Feb 2022 |
Event | International Conference on Computer Vision 2021 - Online Duration: 11 Oct 2021 → 17 Oct 2021 https://iccv2021.thecvf.com/ |
Publication series
Name | 2021 IEEE/CVF International Conference on Computer Vision (ICCV) |
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Publisher | IEEE |
ISSN (Print) | 1550-5499 |
ISSN (Electronic) | 2380-7504 |
Conference
Conference | International Conference on Computer Vision 2021 |
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Abbreviated title | ICCV 2021 |
Period | 11/10/21 → 17/10/21 |
Internet address |
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Visual AI: An Open World Interpretable Visual Transformer
Engineering and Physical Sciences Research Council
1/12/20 → 30/11/26
Project: Research