  We tackle the problem of automatic portrait matting on mobile devices. The
proposed model is aimed at attaining real-time inference on mobile devices with
minimal degradation of model performance. Our model MMNet, based on
multi-branch dilated convolution with linear bottleneck blocks, outperforms the
state-of-the-art model and is orders of magnitude faster. The model can be
accelerated four times to attain 30 FPS on Xiaomi Mi 5 device with moderate
increase in the gradient error. Under the same conditions, our model has an
order of magnitude less number of parameters and is faster than Mobile
DeepLabv3 while maintaining comparable performance. The accompanied
implementation can be found at \url{https://github.com/hyperconnect/MMNet}.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing