How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
The main features of guosheng/refinenet are: Segmentation Architectures, Semantic Segmentation.
Projects with overlapping indexed features include: hyeonwoonoh/deconvnet — DeconvNet : Learning Deconvolution Network for Semantic Segmentation. kolesman/sec — Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation. fyu/dilation — Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the… hszhao/pspnet — by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. junfu1115/danet — Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu. wasidennis/adaptsegnet — Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight).
by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.
DeconvNet : Learning Deconvolution Network for Semantic Segmentation
Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the network definitions and the trained models. You can use this code together with vanilla Caffe to segment images using the pre-trained models. If you want to train the models yourself,…
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu