@inproceedings{SisLab3263,
       booktitle = {2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and  19th International Symposium on Advanced Intelligent Systems  in conjunction with Intelligent Systems Workshop},
           month = {December},
           title = {Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks},
          author = {Van Viet Doan and Duy Hung Nguyen and Quoc Long Tran and Do Van Nguyen and Thanh Ha Le},
            year = {2018},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3263/},
        abstract = {{--}Semantic image segmentation plays a key role in
obtaining pixel-level understanding of images. In recent years,
researchers have tackled this problem by using deep learning
methods instead of traditional computer vision methods (eg [25]).
Because of the development of technologies like autonomous
vehicles and indoor robots, segmentation techniques, that have
not only high accuracy but also the capability of running in
real-time on embedded platform and mobile devices, are in high
demand. In this work, we have proposed a new convolutional
module, named Residual depth-wise separable, and a fast and
efficient convolutional neural network for segmentation. The
proposed method is compared against other state of the art
real-time models. The experiment results illustrate that our
method is efficient in computation while achieves state of the
art performance in term of accuracy}
}