Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M Hospedales

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

Abstract / Description of output

Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated metalearning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for real-world few-shot image classification in practice. To this end, we explore few-shot learning from the perspective of neural architecture, as well as a three stage pipeline of pre-training on external data, meta-training with labelled few-shot tasks, and task-specific fine-tuning on unseen tasks. We investigate questions such as: (1) How pre-training on external data benefits FSL? (2) How state of the art transformer architectures can be exploited? and (3) How to best exploit fine-tuning? Ultimately, we show that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. Our code is available at https://hushell.github.io/pmf.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages9058-9067
Number of pages10
ISBN (Electronic)978-1-6654-6946-3
ISBN (Print)978-1-6654-6947-0
DOIs
Publication statusPublished - 27 Sept 2022
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
- New Orleans, United States
Duration: 19 Jun 202224 Jun 2022
https://cvpr2022.thecvf.com/

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
Abbreviated titleCVPR 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2224/06/22
Internet address

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