Projects per year
Abstract
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction. This naturally raises the question of whether there is any few-shot meta-learning algorithm capable of generalizing across these diverse task types? To support the community in answering this question, we introduce Meta Omnium, a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. We experiment with popular few-shot meta-learning baselines and analyze their ability to generalize across tasks and to transfer knowledge between them. Meta Omnium enables meta-learning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner.
Original language | English |
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Title of host publication | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 7693-7703 |
Number of pages | 11 |
ISBN (Electronic) | 9798350301298 |
ISBN (Print) | 9798350301304 |
DOIs | |
Publication status | Published - 22 Aug 2023 |
Event | The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 - Vancouver Convention Center, Vancouver, Canada Duration: 18 Jun 2023 → 22 Jun 2023 https://cvpr2023.thecvf.com/ |
Publication series
Name | Conference on Computer Vision and Pattern Recognition (CVPR) |
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Publisher | IEEE |
ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 2575-7075 |
Conference
Conference | The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 |
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Abbreviated title | CVPR 2023 |
Country/Territory | Canada |
City | Vancouver |
Period | 18/06/23 → 22/06/23 |
Internet address |
Fingerprint
Dive into the research topics of 'Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn'. Together they form a unique fingerprint.Projects
- 1 Finished
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Signal Processing in the Information Age
Davies, M. (Principal Investigator), Hopgood, J. (Co-investigator), Hospedales, T. (Co-investigator), Mulgrew, B. (Co-investigator), Thompson, J. (Co-investigator), Tsaftaris, S. (Co-investigator) & Yaghoobi Vaighan, M. (Co-investigator)
1/07/18 → 31/03/24
Project: Research