relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3263/
title: Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks
creator: Doan, Van Viet
creator: Nguyen, Duy Hung
creator: Tran, Quoc Long
creator: Nguyen, Do Van
creator: Le, Thanh Ha
subject: Information Technology (IT)
description: —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
date: 2018-12-05
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3263/1/review_2.pdf
identifier:   Doan, Van Viet and Nguyen, Duy Hung and Tran, Quoc Long and Nguyen, Do Van and Le, Thanh Ha  (2018) Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks.  In: 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, 5-8 December 2018, Toyama, Japan.     
relation: http://scis2018.j-soft.org/program.html