@conference {Brachmann2017,
	title = {DSAC - Differentiable RANSAC for camera localization},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {nov},
	pages = {2492{\textendash}2500},
	abstract = {RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.267},
	url = {http://arxiv.org/abs/1611.05705},
	author = {Brachmann, Eric and Krull, Alexander and Nowozin, Sebastian and Shotton, Jamie and Michel, Frank and Gumhold, Stefan and Carsten Rother}
}
@conference {Krull2017,
	title = {PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {2566{\textendash}2574},
	abstract = {State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.275},
	url = {http://arxiv.org/abs/1612.03779},
	author = {Krull, Alexander and Brachmann, Eric and Nowozin, Sebastian and Michel, Frank and Shotton, Jamie and Carsten Rother}
}
@conference {Zheng2015,
	title = {Object proposals estimation in depth image using compact 3D shape manifolds},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {196{\textendash}208},
	abstract = {Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_16},
	author = {Zheng, Shuai and Prisacariu, Victor Adrian and Averkiou, Melinos and Cheng, Ming Ming and Mitra, Niloy J and Shotton, Jamie and Torr, Philip H.S. and Carsten Rother}
}
@article {Shotton2009,
	title = {TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context},
	journal = {International Journal of Computer Vision},
	volume = {81},
	number = {1},
	year = {2009},
	pages = {2{\textendash}23},
	abstract = {This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695-702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). {\textcopyright} 2007 Springer Science+Business Media, LLC.},
	keywords = {Boosting, Conditional random field, Context, image understanding, Layout, Object recognition, Piecewise training, Segmentation, Semantic image segmentation, Textons, Texture},
	issn = {09205691},
	doi = {10.1007/s11263-007-0109-1},
	url = {http://jamie.shotton.org/work/code.html},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
@conference {Shotton2006,
	title = {TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {3951 LNCS},
	year = {2006},
	pages = {1{\textendash}15},
	abstract = {This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating these classifiers in a conditional random field. Efficient training of the model on very large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy are demonstrated on three different databases: i) our own 21-object class database of photographs of real objects viewed under general lighting conditions, poses and viewpoints, ii) the 7-class Corel subset and iii) the 7-class Sowerby database used in [1]. The proposed algorithm gives competitive results both for highly textured (e.g. grass, trees), highly structured (e.g. cars, faces, bikes, aeroplanes) and articulated objects (e.g. body, cow). {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {aeroplanes) and articulated objects (eg body, bikes, cow), faces, g grass, highly structured (eg cars, trees)},
	isbn = {3540338322},
	issn = {16113349},
	doi = {10.1007/11744023_1},
	url = {http://research.microsoft.com/vision/cambridge/recognition/.},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
