Learning twofold heterogeneous multi-task by sharing similar convolution kernel pairs

. Consequently, our model effectively performs cross-task learning while suppresses the intra-redundancy of the entire network. Finally, we conduct end-to-end training. Our experimental results demonstrate the significant competitiveness of our method in comparison with the current state-of-the-art methods.