UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss

@inproceedings{Meister2018UnFlowUL,
  title={UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss},
  author={Simon Meister and Junhwa Hur and Stefan Roth},
  booktitle={AAAI},
  year={2018}
}
In the era of end-to-end deep learning, many advances in computer vision are driven by large amounts of labeled data. In the optical flow setting, however, obtaining dense perpixel ground truth for real scenes is difficult and thus such data is rare. Therefore, recent end-to-end convolutional networks for optical flow rely on synthetic datasets for supervision, but the domain mismatch between training and test scenarios continues to be a challenge. Inspired by classical energy-based optical… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 61 CITATIONS, ESTIMATED 44% COVERAGE

FILTER CITATIONS BY YEAR

2018
2019

CITATION STATISTICS

  • 15 Highly Influenced Citations

  • Averaged 43 Citations per year over the last 3 years

References

Publications referenced by this paper.
SHOWING 1-10 OF 39 REFERENCES

Similar Papers

Loading similar papers…