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We present LS-CRF, a new method for very efficient large-scale training of Conditional Random Fields (CRFs).
Statistical analysis of non-lattice data
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Greedy function approximation: a gradient boosting machine
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
J. Lafferty, A. McCallum, and F. Pereira · 2001
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The complexity of partition functions
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Piecewise training of undirected models
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Large margin methods for structured and interdependent output variables
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Accelerated training of conditional random fields with stochastic gradient methods
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Recovering surface layout from an image
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On partial optimality in multi-label mrfs
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Labelme: a database and web-based tool for image annotation
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Graphical models, exponential families, and variational inference
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Class segmentation and object localization with superpixel neighborhoods
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On parameter learning in CRF-based approaches to object class image segmentation
S. Nowozin, P. V. Gehler, and C. H. Lampert · 2010
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Efficient training for pairwise or higher order CRFs via dual decomposition
N. Komodakis · 2011
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Structured learning and prediction in computer vision
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Decision tree fields
S. Nowozin, C. Rother, S. Bagon, T. Sharp, B. Yao, and P. Kohli · 2011
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SLIC superpixels compared to state-of-the-art superpixel methods
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk · 2012
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Three things everyone should know to improve object retrieval
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Large-scale machine learning with stochastic gradient descent
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The Pascal visual object classes (VOC) challenge
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Learning graphical model parameters with approximate marginals inference
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A comparative study of modern inference techniques for discrete energy minimization problem
J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, J. Lellmann, N. Komodakis, and C. Rother · 2013
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