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This paper describes a fast and accurate semantic image segmentation approach that encodes not only the discriminative features from deep neural networks, but also the high-order context compatibility among adjacent objects as well as low level image features.
Auto-context and its application to high-level vision tasks
Tu, Z.: · 2008
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Associative hierarchical crfs for object class image segmentation
Russell, C., Kohli, P., Torr, P.H., et al.: · 2009
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Real-time o (1) bilateral filtering
Yang, Q., Tan, K.H., Ahuja, N.: · 2009
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Probabilistic graphical models, massachusetts (2009)
Koller, D., Friedman, N.: · 2009
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Stacked hierarchical labeling
Munoz, D., Bagnell, J.A., Hebert, M.: · 2010
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Fast high-dimensional filtering using the permutohedral lattice
Adams, A., Baek, J., Davis, M.A.: · 2010
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Guided image filtering
He, K., Sun, J., Tang, X.: · 2010
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Learning message-passing inference machines for structured prediction
Ross, S., Munoz, D., Hebert, M., Bagnell, J.A.: · 2011
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Semantic contours from inverse detectors
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Efficient inference in fully connected crfs with gaussian edge potentials
Krähenbühl, P., Koltun, V.: · 2012
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: · 2014
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Learning deep structured models
Chen, L.C., Schwing, A.G., Yuille, A.L., Urtasun, R.: · 2014
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Filter-based mean-field inference for random fields with higher-order terms and product label-spaces
Vineet, V., Warrell, J., Torr, P.H.: · 2014
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Conditional random fields as recurrent neural networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: · 2015
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Efficient piecewise training of deep structured models for semantic segmentation
Lin, G., Shen, C., Reid, I., et al.: · 2015
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A generic cnn-crf model for semantic segmentation
Kirillov, A., Schlesinger, D., Forkel, W., Zelenin, A., Zheng, S., Torr, P., Rother, C.: · 2015
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Higher order potentials in end-to-end trainable conditional random fields
Arnab, A., Jayasumana, S., Zheng, S., Torr, P.: · 2015
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Deeply learning the messages in message passing inference
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Lin, G., Shen, C., Reid, I., van den Hengel, A.: · 2015
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Semantic image segmentation via deep parsing network
Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: · 2015
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He, K., Sun, J.: · 2015
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Deep image: Scaling up image recognition
Wu, R., Yan, S., Shan, Y., Dang, Q., Sun, G.: · 2015
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Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
Papandreou, G., Chen, L.C., Murphy, K., Yuille, A.L.: · 2015
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Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K., Yuille, A.L.: · 2015
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