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State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs).
Exact inference in multi-label crfs with higher order cliques
Ramalingam, S., Kohli, P., Alahari, K., Torr, P.H.: · 2008
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Associative hierarchical crfs for object class image segmentation
Russell, C., Kohli, P., Torr, P.: · 2009
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Robust higher order potentials for enforcing label consistency
Kohli, P., Torr, P.H.: · 2009
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Superparsing: Scalable nonparametric image parsing with superpixels
Tighe, J., Lazebnik, S.: · 2010
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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Nonparametric scene parsing via label transfer
Liu, C., Yuen, J., Torralba, A.: · 2011
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Contour detection and hierarchical image segmentation
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: · 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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Nonparametric image parsing using adaptive neighbor sets
Eigen, D., Fergus, R.: · 2012
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Scene parsing with multiscale feature learning, purity trees, and optimal covers
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 2012
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Generating sequences with recurrent neural networks
Graves, A.: · 2013
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Finding things: Image parsing with regions and per-exemplar detectors
Tighe, J., Lazebnik, S.: · 2013
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Nonparametric scene parsing with adaptive feature relevance and semantic context
Singh, G., Kosecka, J.: · 2013
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Exploring compositional high order pattern potentials for structured output learning
Li, Y., Tarlow, D., Zemel, R.: · 2013
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Recurrent convolutional neural networks for scene parsing
Pinheiro, P.H., Collobert, R.: · 2013
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Caffe: An open source convolutional architecture for fast feature embedding (2013)
Jia, Y.: · 2013
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Towards end-to-end speech recognition with recurrent neural networks
Graves, A., Jaitly, N.: · 2014
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Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
Cited alongside, same era.
Simultaneous detection and segmentation
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2014
Cited alongside, same era.
Recurrent neural network regularization
Zaremba, W., Sutskever, I., Vinyals, O.: · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Convolutional feature masking for joint object and stuff segmentation
Dai, J., He, K., Sun, J.: · 2015
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Dai, J., He, K., Sun, J.: · 2015
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Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2015
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Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., Han, B.: · 2015
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Region-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2015
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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Contextually constrained deep networks for scene labeling
Kekeç, T., Emonet, R., Fromont, E., Trémeau, A., Wolf, C.: · 2014
Cited alongside, same era.
Recursive context propagation network for semantic scene labeling
Sharma, A., Tuzel, O., Liu, M.: · 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
Cited alongside, same era.
Efficient piecewise training of deep structured models for semantic segmentation
Lin, G., Shen, C., Reid, I., et al.: · 2015
Cited alongside, same era.
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
Cited alongside, same era.
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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.: · 2015
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Fully connected deep structured networks
Schwing, A.G., Urtasun, R.: · 2015
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Feedforward semantic segmentation with zoom-out features
Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: · 2015
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: · 2015
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Renet: A recurrent neural network based alternative to convolutional networks
Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A., Bengio, Y.: · 2015
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Gated feedback recurrent neural networks
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: · 2015
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Scene labeling with lstm recurrent neural networks
Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: · 2015
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Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 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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