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Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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A local learning algorithm for dynamic feedforward and recurrent networks
Schmidhuber, J.: · 1989
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Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., Frasconi, P.: · 1994
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Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Efficient graph-based image segmentation
Felzenszwalb, P.F., Huttenlocher, D.P.: · 2004
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Decomposing a scene into geometric and semantically consistent regions
Gould, S., Fulton, R., Koller, D.: · 2009
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Offline handwriting recognition with multidimensional recurrent neural networks
Graves, A., Schmidhuber, J.: · 2009
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Superparsing: scalable nonparametric image parsing with superpixels
Tighe, J., Lazebnik, S.: · 2010
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Efficiently selecting regions for scene understanding
Kumar, M.P., Koller, D.: · 2010
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Real-time plane segmentation using rgb-d cameras
Holz, D., Holzer, S., Rusu, R.B., Behnke, S.: · 2011
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Pylon model for semantic segmentation
Lempitsky, V., Vedaldi, A., Zisserman, A.: · 2011
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Sift flow: Dense correspondence across scenes and its applications
Liu, C., Yuen, J., Torralba, A.: · 2011
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Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: · 2012
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Rgb-(d) scene labeling: Features and algorithms
Ren, X., Bo, L., Fox, D.: · 2012
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Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: · 2012
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A category-level 3d object dataset: Putting the kinect to work
Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K., Darrell, T.: · 2013
Cited alongside, same era.
Sun3d: A database of big spaces reconstructed using sfm and object labels
Xiao, J., Owens, A., Torralba, A.: · 2013
Cited alongside, same era.
Hierarchical semantic labeling for task-relevant rgb-d perception
Wu, C., Lenz, I., Saxena, A.: · 2014
Cited alongside, same era.
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.
Learning rich features from rgb-d images for object detection and segmentation
Deep sliding shapes for amodal 3d object detection in rgb-d images
Song, S., Xiao, J.: · 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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Scene labeling with lstm recurrent neural networks
Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: · 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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Sun rgb-d: A rgb-d scene understanding benchmark suite
Song, S., Lichtenberg, S.P., Xiao, J.: · 2015
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Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: · 2014
Cited alongside, same era.
Recurrent convolutional neural networks for scene labeling
Pinheiro, P., Collobert, R.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Toward real-time indoor semantic segmentation using depth information
Couprie, C., Farabet, C., Najman, L., LeCun, Y.: · 2014
Cited alongside, same era.
Generating semantically precise scene graphs from textual descriptions for improved image retrieval
Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., Manning, C.D.: · 2015
Cited alongside, same era.
Integrating geometrical context for semantic labeling of indoor scenes using rgbd images
Khan, S.H., Bennamoun, M., Sohel, F., Togneri, R., Naseem, I.: · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Kendall, A., Badrinarayanan, V., Cipolla, R.: · 2015
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Indoor scene understanding with rgb-d images: Bottom-up segmentation, object detection and semantic segmentation
Gupta, S., Arbeláez, P., Girshick, R., Malik, J.: · 2015
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Unsupervised joint feature learning and encoding for rgb-d scene labeling
Wang, A., Lu, J., Cai, J., Wang, G., Cham, T.J.: · 2015
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Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation
Stollenga, M.F., Byeon, W., Liwicki, M., Schmidhuber, J.: · 2015
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Automatic photo adjustment using deep neural networks
Yan, Z., Zhang, H., Wang, B., Paris, S., Yu, Y.: · 2016
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Semantic object parsing with local-global long short-term memory
Liang, X., Shen, X., Xiang, D., Feng, J., Lin, L., Yan, S.: · 2016
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Deep contrast learning for salient object detection
Li, G., Yu, Y.: · 2016
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Combining semantic and geometric features for object class segmentation of indoor scenes
Husain, F., Schulz, H., Dellen, B., Torras, C., Behnke, S.: · 2017
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