FourierNet: Compact Mask Representation for Instance Segmentation Using Differentiable Shape Decoders

@article{Benbarka2020FourierNetCM,
  title={FourierNet: Compact Mask Representation for Instance Segmentation Using Differentiable Shape Decoders},
  author={Nuri Benbarka and Hamd ul Moqeet Riaz and Andreas Zell},
  journal={2020 25th International Conference on Pattern Recognition (ICPR)},
  year={2020},
  pages={7833-7840}
}
We present FourierNet, a single shot, anchor-free, fully convolutional instance segmentation method that predicts a shape vector. Consequently, this shape vector is converted into the masks' contour points using a fast numerical transform. Compared to previous methods, we introduce a new training technique, where we utilize a differentiable shape decoder, which manages the automatic weight balancing of the shape vector's coefficients. We used the Fourier series as a shape encoder because of its… 
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