Corpus ID: 195750617

Unsupervised Learning of Object Keypoints for Perception and Control

@inproceedings{Kulkarni2019UnsupervisedLO,
  title={Unsupervised Learning of Object Keypoints for Perception and Control},
  author={T. Kulkarni and A. Gupta and Catalin Ionescu and Sebastian Borgeaud and M. Reynolds and Andrew Zisserman and V. Mnih},
  booktitle={NeurIPS},
  year={2019}
}
  • T. Kulkarni, A. Gupta, +4 authors V. Mnih
  • Published in NeurIPS 2019
  • Computer Science
  • 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. [...] Key Method Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods…Expand Abstract
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