@conference {Levinkov2017,
	title = {Joint graph decomposition \& node labeling: Problem, algorithms, applications},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	pages = {1904{\textendash}1912},
	abstract = {We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.206},
	author = {Levinkov, Evgeny and Uhrig, Jonas and Tang, Siyu and Omran, Mohamed and Insafutdinov, Eldar and Kirillov, Alexander and Carsten Rother and Brox, Thomas and Schiele, Bernt and Bj{\"o}rn Andres}
}
@conference {Kirillov2017,
	title = {Joint training of generic CNN-CRF models with stochastic optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10112 LNCS},
	year = {2017},
	pages = {221{\textendash}236},
	abstract = {We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.},
	isbn = {9783319541839},
	issn = {16113349},
	doi = {10.1007/978-3-319-54184-6_14},
	url = {http://host.robots.ox.ac.uk:8080/leaderboard},
	author = {Kirillov, A and Schlesinger, D and Zheng, S and Savchynskyy, B and Torr, P. H.S. and Carsten Rother}
}
@article {storath2016jump,
	title = {Jump-penalized least absolute values estimation of scalar or circle-valued signals},
	journal = {Information and Inference},
	volume = {6},
	year = {2017},
	pages = {225{\textendash}245},
	doi = {10.1093/imaiai/iaw022},
	author = {Martin Storath and Andreas Weinmann and Michael Unser}
}
@conference {Berger2016a,
	title = {Joint Recursive Monocular Filtering of Camera Motion and Disparity Map},
	booktitle = {38th German Conference on Pattern Recognition},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	address = {Hannover},
	doi = {1606.02092},
	url = {https://arxiv.org/abs/1606.02092},
	author = {Johannes Berger and Christoph Schn{\"o}rr}
}
@conference {Berger2016a,
	title = {Joint Recursive Monocular Filtering of Camera Motion and Disparity Map},
	booktitle = {38th German Conference on Pattern Recognition},
	year = {2016},
	note = {in press},
	author = {Johannes Berger and Christoph Schn{\"o}rr}
}
@article {7468481,
	title = {Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm},
	journal = {IEEE Transactions on Image Processing},
	volume = {25},
	year = {2016},
	pages = {3384 - 3394},
	keywords = {Active contours, Computational modeling, Image segmentation, Manifolds, Shape, Three-dimensional displays, TV},
	issn = {1057-7149},
	doi = {10.1109/TIP.2016.2567068},
	author = {Anca Stefanoiu and Andreas Weinmann and Martin Storath and Nassir Navab and Maximilian Baust}
}
@article {diego_12_joint,
	title = {Joint SpatioTemporal Alignment of Sequences},
	journal = {IEEE Transactions on Multimedia (TMM)},
	volume = {PP (99)},
	year = {2012},
	note = {1},
	pages = {1},
	doi = {10.1109/TMM.2013.2247390},
	author = {Ferran Diego and Serrat, J. and López, A. M.}
}
@conference {Vicente2009,
	title = {Joint optimization of segmentation and appearance models},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {755{\textendash}762},
	abstract = {Many interactive image segmentation approaches use an objective function which includes appearance models as an unknown variable. Since the resulting optimization problem is NP-hard the segmentation and appearance are typically optimized separately, in an EM-style fashion. One contribution of this paper is to express the objective function purely in terms of the unknown segmentation, using higher-order cliques. This formulation reveals an interesting bias of the model towards balanced segmentations. Furthermore, it enables us to develop a new dual decomposition optimization procedure, which provides additionally a lower bound. Hence, we are able to improve on existing optimizers, and verify that for a considerable number of real world examples we even achieve global optimality. This is important since we are able, for the first time, to analyze the deficiencies of the model. Another contribution is to establish a property of a particular dual decomposition approach which involves convex functions depending on foreground area. As a consequence, we show that the optimal decomposition for our problem can be computed efficiently via a parametric maxflow algorithm. {\textcopyright}2009 IEEE.},
	isbn = {9781424444205},
	doi = {10.1109/ICCV.2009.5459287},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
