• Corpus ID: 238743890

Unsupervised Object Learning via Common Fate

@article{Tangemann2021UnsupervisedOL,
  title={Unsupervised Object Learning via Common Fate},
  author={Matthias Tangemann and Steffen Schneider and Julius von K{\"u}gelgen and Francesco Locatello and Peter Gehler and Thomas Brox and Matthias Kummerer and Matthias Bethge and Bernhard Scholkopf},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06562}
}
Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained on the masks of the background and the moving objects, respectively. Third… 

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