ryanjdick/flownet-tests

An experiment to evaluate various improvements to the Flownet architecture for optical flow estimation.

flownet-tests

An experiment to evaluate the effectiveness of improvements made on top of the Flownet architecture for optical flow prediction. Specifically, this project looks into:

1. Using a CRF as a post-processing step.
2. Using dilated convolutions to increase the resolution of the predictions made before upsampling.

More details to come.

Citations

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

@article{DBLP:journals/corr/FischerDIHHGSCB15,
  author    = {Philipp Fischer and
               Alexey Dosovitskiy and
               Eddy Ilg and
               Philip H{\"{a}}usser and
               Caner Hazirbas and
               Vladimir Golkov and
               Patrick van der Smagt and
               Daniel Cremers and
               Thomas Brox},
  title     = {FlowNet: Learning Optical Flow with Convolutional Networks},
  journal   = {CoRR},
  volume    = {abs/1504.06852},
  year      = {2015},
  url       = {http://arxiv.org/abs/1504.06852},
  archivePrefix = {arXiv},
  eprint    = {1504.06852},
  timestamp = {Wed, 07 Jun 2017 14:41:04 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/FischerDIHHGSCB15},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}