1樓：taigw
and Jian Sun. "Boxsup: Exploiting bounding boxes to supervise
convolutional networks for semantic segmentation." ICCV. 2015.
Abstract: propose a method only using bounding box annotation. The basic idea is to iterate between automatically generating region proposals and training CNNs.
Good results obtained on PASCAL-2012 and PASCAL-CONTEXT.
2，Rajchl, Martin, et al. "DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks.
" arXiv preprint arXiv:1605.07866 (2016).
Abstract: Introduced a network for volumetric segmentation that learns from sparsely annotated volumetric images. It extended the U-Net to 3D and performs on-th-fly elastic deformation for efficient data agumentation during training.
2, Lin, Di, et al. "ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation.
" arXiv preprint arXiv:1604.05144 (2016).
Abstract: The algorithm is based on a graphic model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. Excellent results were shown on PASCAL VOC and PASCAL CONTEXT datasets.
三， 基於image-tags的學習
1, Pathak, Deepak, Philipp
Krahenbuhl, and Trevor Darrell. "Constrained convolutional neural
and Joachim M. Buhmann. "Towards weakly supervised semantic segmentation
by means of multiple instance and multitask learning." CVPR 2010.
Abstract: Semantic Texton Forest (STF) is used as the basic framework and extended for the Multiple Instance Leraning setting. Multitask learning (MTL) is used to
regularize the solution. Here, an external task of geometric context
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