Universal Representation Learning from Multiple Domains for Few-shot Classification

Wei-Hong Li, Xialei Liu, Hakan Bilen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021
PublisherInstitute of Electrical and Electronics Engineers
Pages9506-9515
Number of pages10
ISBN (Electronic)978-1-6654-2812-5
ISBN (Print)978-1-6654-2813-2
DOIs
Publication statusPublished - 28 Feb 2022
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021
https://iccv2021.thecvf.com/

Publication series

Name2021 IEEE/CVF International Conference on Computer Vision (ICCV)
PublisherIEEE
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Period11/10/2117/10/21
Internet address

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