Abstract / Description of output
Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision. However, this method does not explicitly guarantee that the learned landmarks are consistent with changes between different instances of the same object, such as different facial identities. In this paper, we develop a new perspective on the equivariance approach by noting that dense landmark detectors can be interpreted as local image descriptors equipped with invariance to intra-category variations. We then propose a direct method to enforce such an invariance in the standard equivariant loss. We do so by exchanging descriptor vectors between images of different object instances prior to matching them geometrically. In this manner, the same vectors must work regardless of the specific object identity considered. We use this approach to learn vectors that can simultaneously be interpreted as local descriptors and dense landmarks, combining the advantages of both. Experiments on standard benchmarks show that this approach can match, and in some cases surpass state-of-the-art performance amongst existing methods that learn landmarks without supervision. Code is available at www.robots.ox.ac.uk/˜vgg/research/DVE/.
Original language | English |
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Title of host publication | 2019 IEEE/CVF International Conference on Computer Vision (ICCV) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 6360-6370 |
Number of pages | 11 |
ISBN (Electronic) | 978-1-7281-4803-8 |
ISBN (Print) | 978-1-7281-4804-5 |
DOIs | |
Publication status | Published - 27 Feb 2020 |
Event | International Conference on Computer Vision 2019 - Seoul, Korea, Republic of Duration: 27 Oct 2019 → 2 Nov 2019 http://iccv2019.thecvf.com/ |
Publication series
Name | |
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Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 1550-5499 |
ISSN (Electronic) | 2380-7504 |
Conference
Conference | International Conference on Computer Vision 2019 |
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Abbreviated title | ICCV 2019 |
Country/Territory | Korea, Republic of |
City | Seoul |
Period | 27/10/19 → 2/11/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Unsupervised learning
- convolutional neural networks
- pose estimation