- 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 ...
Expand abstract - Publication status:
- In press
- Peer review status:
- Peer reviewed
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- Publisher's Version
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- Publisher:
- Curran Associates Publisher's website
- Volume:
- 31
- Pages:
- 4016-4027
- Publication date:
- 2018-12-31
- Acceptance date:
- 2018-09-05
- Pubs id:
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pubs:943529
- URN:
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uri:f562c769-774d-4d49-821d-2189bb213d00
- UUID:
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uuid:f562c769-774d-4d49-821d-2189bb213d00
- Local pid:
- pubs:943529
- 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
Conference item
Unsupervised learning of object landmarks through conditional image generation
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