└── README.md


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 1 | 
 2 | # Interactive Image Segmentation 
 3 | 
 4 | ## Deep learning approach 
 5 | 
 6 | ### click & scribble
 7 | -    [Deep Interactive Object Selection](https://arxiv.org/pdf/1603.04042.pdf) - N Xu  et al.,  2016 CVPR  
 8 | 		*  positive and negative clicks
 9 | 		*  FCN + refinement by Graph Cut 
10 | 		
11 | -    [Regional Interactive Image Segmentation Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237559) - JH Liew  et al.,  2017 ICCV  
12 | 		* click based 
13 | 		*  global branch + local regional refinement 
14 | 		
15 | -    [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://arxiv.org/pdf/1707.00652.pdf) - G Wang  et al.,  2017 arXiv  
16 |        * click/ scribble based 
17 |        *  Geodesic Distance Transformation 
18 |        *  Refine Network
19 | 
20 | -    [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://arxiv.org/pdf/1711.09081.pdf](https://arxiv.org/pdf/1711.09081.pdf)) - KK Maninis  et al.,  2018 CVPR
21 |        *  Four extreme points 
22 |        *  [[Pytorch code]](https://github.com/scaelles/DEXTR-PyTorch/)
23 | 
24 | 
25 | -    [Interactive Image Segmentation with Latent Diversity](http://vladlen.info/papers/interactive-segmentation.pdf) - Z L  et al.,  2018 CVPR
26 | 		*  Minimizing the number of clicks
27 | 		*  Multi-modality
28 | 		*  Multiple choice learning
29 | -    [Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++](https://arxiv.org/pdf/1803.09693.pdf) - D Acuna  et al.,  2018 CVPR  
30 | 		* [ Polygon-RNN](https://www.cs.toronto.edu/~urtasun/publications/castrejon_etal_cvpr17.pdf)
31 | 	
32 | ### natural language
33 | 
34 | -    [Guide Me: Interacting with Deep Networks](https://arxiv.org/pdf/1803.11544.pdf) - C Rupprecht et al.,  2018 CVPR 
35 | 
36 | ## weakly/semi supervised learning 
37 | 
38 | -    [Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-319-46493-0_42) - A Kolesnikov  et al.,  2016 ECCV
39 | 
40 | -    [What’s the Point: Semantic Segmentation with Point Supervision](https://link.springer.com/chapter/10.1007/978-3-319-46478-7_34) - A Bearman  et al.,  2016 ECCV
41 | 
42 | -    [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Khoreva_Simple_Does_It_CVPR_2017_paper.pdf) - A Khoreva et al.,  2017 CVPR
43 | 
44 | -    [Exploiting saliency for object segmentation from image level labels](http://openaccess.thecvf.com/content_cvpr_2017/papers/Oh_Exploiting_Saliency_for_CVPR_2017_paper.pdf) - SH Oh et al.,  2017 CVPR
45 | 			
46 | 
47 | ## Graph based approach 
48 | 
49 | -    [Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images](http://bitsearch.blogspot.kr/2014/01/understanding-graph-cuts-for-image.html) - YY Boykov  et al.,  2001 ICCV
50 | 
51 | -    [“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts](https://cvg.ethz.ch/teaching/cvl/2012/grabcut-siggraph04.pdf) - C Rother  et al.,  2004 TOG
52 | 
53 | -    [Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S14-03.pdf) - D Lin et al.,  2016 CVPR
54 | 		
55 | 					
56 | 
57 | # Interactive Video Segmentation  
58 | 
59 | ## Deep learning approach 
60 | 
61 | ### natural language
62 | -    [Video Object Segmentation with Language Referring Expressions](https://arxiv.org/pdf/1803.08006.pdf) - A Khoreva et al.,  2018 arXiv 
63 | 


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