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Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation
The main features of kolesman/sec are: Segmentation Architectures, Semantic Segmentation.
Projects with overlapping indexed features include: 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. fyu/dilation — Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the… guosheng/refinenet. hyeonwoonoh/deconvnet — DeconvNet : Learning Deconvolution Network for Semantic Segmentation. 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.
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,…
DeconvNet : Learning Deconvolution Network for Semantic Segmentation