Rethinking Spatiotemporal Feature Learning For Video Understanding

@article{Xie2017RethinkingSF,
  title={Rethinking Spatiotemporal Feature Learning For Video Understanding},
  author={Saining Xie and Chen Sun and Jonathan Huang and Zhuowen Tu and Kevin Murphy},
  journal={CoRR},
  year={2017},
  volume={abs/1712.04851}
}
In this paper we study 3D convolutional networks for video understanding tasks. Our starting point is the stateof-the-art I3D model of [3], which “inflates” all the 2D filters of the Inception architecture to 3D. We first consider “deflating” the I3D model at various levels to understand the role of 3D convolutions. Interestingly, we found that 3D convolutions at the top layers of the network contribute more than 3D convolutions at the bottom layers, while also being computationally more… CONTINUE READING
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