Conference item
Unsupervised learning of object landmarks through conditional image generation
- Abstract:
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We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision. We cast this as the problem of generating images that combine the appearance of the object as seen in a first example image with the geometry of the object as seen in a second example image, where the two examples differ by a viewpoint change and/or an object deformation. In order to factorize appearance and geometry, we introduce a tight bottleneck ...
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- Publication status:
- In press
- Peer review status:
- Peer reviewed
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Authors
Funding
Clarendon Fund
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Bibliographic Details
- Publisher:
- Curran Associates Publisher's website
- Journal:
- Thirty-second Conference on Neural Information Processing Systems (NIPS 2018) Journal website
- Volume:
- 31
- Pages:
- 4016-4027
- Host title:
- Advances in Neural Information Processing Systems 31
- Publication date:
- 2018-12-31
- Acceptance date:
- 2018-09-05
- Source identifiers:
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943529
Item Description
- Pubs id:
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pubs:943529
- UUID:
-
uuid:f562c769-774d-4d49-821d-2189bb213d00
- Local pid:
- pubs:943529
- Deposit date:
- 2018-11-16
Terms of use
- Copyright holder:
- Neural Information Processing Systems Foundation, Inc
- Copyright date:
- 2018
- Notes:
- © 2018 Neural Information Processing Systems Foundation, Inc. This paper has been presented at the Thirty-second Conference on Neural Information Processing Systems (NIPS 2018). It is available at https://papers.nips.cc/paper/7657-unsupervised-learning-of-object-landmarks-through-conditional-image-generation
- Licence:
- CC Attribution (CC BY)
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