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Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection.
Parallel and deterministic algorithms from mrfs: Surface reconstruction
Geiger, D. and Girosi, F · 1991
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A common framework for image segmentation
Geiger, D. and Yuille, A · 1991
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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A Wavelet Tour of Signal Processing
Mallat, S · 1999
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Multiscale conditional random fields for image labeling
He, X., Zemel, R. S., and Carreira-Perpindn, M · 2004
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Grabcut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., and Blake, A · 2004
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Computational analysis and learning for a biologically motivated model of boundary detection
Kokkinos, I., Deriche, R., Faugeras, O., and Maragos, P · 2008
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Robust higher order potentials for enforcing label consistency
Kohli, P., Ladicky, L., and Torr, P. H · 2009
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Associative hierarchical crfs for object class image segmentation
Ladicky, L., Russell, C., Kohli, P., and Torr, P. H · 2009
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Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
Shotton, J., Winn, J., Rother, C., and Criminisi, A · 2009
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Fast high-dimensional filtering using the permutohedral lattice
Adams, A., Baek, J., and Davis, M. A · 2010
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Harmony potentials for joint classification and segmentation
Gonfaus, J. M., Boix, X., Van de Weijer, J., Bagdanov, A. D., Serrat, J., and Gonzalez, J · 2010
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Semantic contours from inverse detectors
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., and Malik, J · 2011
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Efficient inference in fully connected crfs with gaussian edge potentials
Krähenbühl, P. and Koltun, V · 2011
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Pylon model for semantic segmentation
Lempitsky, V., Vedaldi, A., and Zisserman, A · 2011
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Are spatial and global constraints really necessary for segmentation?
Lucchi, A., Li, Y., Boix, X., Smith, K., and Fua, P · 2011
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Cpmc: Automatic object segmentation using constrained parametric min-cuts
Carreira, J. and Sminchisescu, C · 2012
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Semantic segmentation with second-order pooling
Carreira, J., Caseiro, R., Batista, J., and Sminchisescu, C · 2012
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Fast approximate energy minimization with label costs
Delong, A., Osokin, A., Isack, H. N., and Boykov, Y · 2012
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Learning a dictionary of shape epitomes with applications to image labeling
Chen, L.-C., Papandreou, G., and Yuille, A · 2013
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Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., and LeCun, Y · 2013
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Fast image scanning with deep max-pooling convolutional neural networks
Giusti, A., Ciresan, D., Masci, J., Gambardella, L., and Schmidhuber, J · 2013
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Parameter learning and convergent inference for dense random fields
Krähenbühl, P. and Koltun, V · 2013
Eigen, D. and Fergus, R · 2014
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The pascal visual object classes challenge – a retrospective
Everingham, M., Eslami, S. M. A., Gool, L. V., Williams, C. K. I., Winn, J., and Zisserma, A · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Caffe: Convolutional architecture for fast feature embedding
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2014
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y · 2013
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Selective search for object recognition
Uijlings, J., van de Sande, K., Gevers, T., and Smeulders, A · 2013
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Discriminative re-ranking of diverse segmentations
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Multiscale combinatorial grouping
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Material recognition in the wild with the materials in context database
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Feedforward semantic segmentation with zoom-out features
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Very deep convolutional networks for large-scale image recognition
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Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
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Part-based r-cnns for fine-grained category detection
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Weakly- and semi-supervised learning of a DCNN for semantic image segmentation
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Towards unified depth and semantic prediction from a single image
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Conditional random fields as recurrent neural networks
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