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Unsupervised learning of object keypoints for perception and control

Abstract:

The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordina...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher:
Curran Associates Publisher's website
Volume:
32
Pages:
10692-10702
Host title:
Advances in Neural Information Processing Systems 32: 32nd Conference on Neural Information Processing Systems (NeurIPS 2019)
Publication date:
2020-06-01
Acceptance date:
2019-09-04
Event title:
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
Event location:
Vancouver, Canada
Event website:
https://nips.cc/Conferences/2019
Event start date:
2019-12-08T00:00:00Z
Event end date:
2019-12-14T00:00:00Z
ISSN:
1049-5258
ISBN:
9781713807933
Language:
English
Keywords:
Pubs id:
1118225
Local pid:
pubs:1118225
Deposit date:
2020-07-20

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