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


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 publicationInternational Conference on Computer Vision 2021
Number of pages11
Publication statusAccepted/In press - 22 Jul 2021
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021


ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Internet address


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