文献类型：期刊文献

英文题名：Multi-task GANs for semantic segmentation and depth completion with cycle consistency

作者：Zhang, Chongzhen[1]; Tang, Yang[1]; Zhao, Chaoqiang[1]; Sun, Qiyu[1]; Ye, Zhencheng[1]; Kurths, Jürgen[2,3]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Potsdam Institute for Climate Impact Research, Potsdam, 14473, Germany; [3] Institute of Physics, Humboldt University of Berlin, Berlin, 12489, Germany

年份：2020

外文期刊名：arXiv

收录：EI(收录号：20210012768)

语种：英文

外文关键词：Benchmarking - Computer vision - Generative adversarial networks - Image enhancement - Semantic Web - Semantics

摘要：Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, like changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this paper, we propose multi-task generative adversarial networks (Multi-task GANs), which are not only competent in semantic segmentation and depth completion, but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multi-scale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on Cityscapes dataset and KITTI depth completion benchmark show that the Multi-task GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks. ? 2020, CC BY.