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Sebastian Borgeaud
34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
# Transporter - Unsupervised Learning of Object Keypoints for Perception and Control
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This directory contains a [Sonnet](https://sonnet.dev) implementation of
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the Transporter architecture and a notebook explaining how the model can be used
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for keypoint inference. To launch the notebook in Google colab, [click here](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/transporter/transporter_example.ipynb).
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The Transporter is a neural network architecture for discovering concise
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geometric object representations in terms of keypoints or image-space
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coordinates. Our method learns from raw video frames in a fully unsupervised
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manner, by transporting learnt image features between video frames using a
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keypoint bottleneck. The discovered keypoints track objects and object parts
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across long time-horizons more accurately than recent similar methods.
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For details, see our
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paper [Unsupervised Learning of Object Keypoints for Perception and Control](https://arxiv.org/abs/1906.11883).
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If you use the code here please cite this paper.
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> Tejas Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih. *Unsupervised Learning of Object Keypoints for Perception and Control*. NeurIPS 2019. [\[arXiv\]](https://arxiv.org/abs/1906.11883).
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## Contributors
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* Tejas Kulkarni <tkulkarni@google.com>
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* Ankush Gupta <ankushgupta@google.com>
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* Catalin Ionescu
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* Sebastian Borgeaud
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* Malcolm Reynolds
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* Andrew Zisserman
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* Volodymyr Mnih
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## Disclaimer
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This is not an official Google product.
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