Learning Image Representations Tied to Egomotion from Unlabeled Video

@article{Jayaraman2017LearningIR,
  title={Learning Image Representations Tied to Egomotion from Unlabeled Video},
  author={Dinesh Jayaraman and Kristen Grauman},
  journal={International Journal of Computer Vision},
  year={2017},
  volume={125},
  pages={136-161}
}
Understanding how images of objects and scenes behave in response to specific egomotions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose a new “embodied” visual learning paradigm, exploiting proprioceptive motor signals to train visual representations from egocentric video with no manual supervision. Specifically, we enforce that our learned features exhibit equivariance i… CONTINUE READING
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