Abstract
We explore semantic correspondence estimation through the lens of unsupervised learning. We thoroughly evaluate several recently proposed unsupervised methods across multiple challenging datasets using a standardized evaluation protocol where we vary factors such as the backbone architecture, the pre-training strategy, and the pre-training and finetuning datasets. To better understand the failure modes of these methods, and in order to provide a clearer path for improvement, we provide a new diagnostic framework along with a new performance metric that is better suited to the semantic matching task. Finally, we introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training. This results in significantly better matching performance compared to current state-of-the-art methods.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part X |
Editors | Shai Avidan, Gabriel Brostow, Moustapha Cissé |
Publisher | Springer |
Pages | 125-142 |
Number of pages | 31 |
ISBN (Electronic) | 978-3-031-20080-9 |
ISBN (Print) | 978-3-031-20079-3 |
DOIs | |
Publication status | Published - 23 Oct 2022 |
Event | European Conference on Computer Vision 2022 - Israel, Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022 https://eccv2022.ecva.net/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Cham |
Volume | 13690 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | European Conference on Computer Vision 2022 |
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Abbreviated title | ECCV 2022 |
Country/Territory | Israel |
City | Tel Aviv |
Period | 23/10/22 → 27/10/22 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- semantic correspondence
- self-supervised learning