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In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs).
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Model compression
Buciluă, C., Caruana, R., Niculescu-Mizil, A.: · 2006
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CBCL StreetScenes challenge framework
Bileschi, S.: · 2007
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Segmentation and recognition using structure from motion point clouds
Brostow, G.J., Shotton, J., Cipolla, R.: · 2008
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LabelMe: a database and web-based tool for image annotation
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: · 2008
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Semantic object classes in video: A high-definition ground truth database
Brostow, G.J., Fauqueur, J., Cipolla, R.: · 2009
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Robust higher order potentials for enforcing label consistency
Kohli, P., Ladický, L., Torr, P.H.S.: · 2009
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Robust multi-person tracking from a mobile platform
Ess, A., Leibe, B., Schindler, K., Gool, L.V.: · 2009
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Superparsing: Scalable nonparametric image parsing with superpixels
Tighe, J., Lazebnik, S.: · 2010
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What, where and how many? Combining object detectors and CRFs
Ladický, L., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: · 2010
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Deconvolutional networks
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: · 2010
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On optimization methods for deep learning
Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Ng, A.Y.: · 2011
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Cool world: domain adaptation of virtual and real worlds for human detection using active learning
Vazquez, D., Lopez, A., Ponsa, D., Marin, J.: · 2011
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Indoor segmentation and support inference from RGBD images
N. Silberman, D.H., Kohli, P., Fergus, R.: · 2012
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Road scene segmentation from a single image
Álvarez, J.M., Gevers, T., LeCun, Y., López, A.M.: · 2012
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Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: · 2013
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Urban 3D semantic modelling using stereo vision
Sengupta, S., Greveson, E., Shahrokni, A., Torr, P.H.S.: · 2013
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Efficient 3-D scene analysis from streaming data
Hu, H., Munoz, D., Bagnell, J.A., Hebert, M.: · 2013
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Mesh based semantic modelling for indoor and outdoor scenes
Valentin, J.P.C., Sengupta, S., Warrell, J., Shahrokni, A., Torr, P.H.S.: · 2013
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: · 2013
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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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Learning deep features for scene recognition using places database
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: · 2014
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Joint semantic segmentation and 3D reconstruction from monocular video
Kundu, A., Li, Y., Dellaert, F., Li, F., Rehg, J.M.: · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Learning deep structured models
Chen, L.C., Schwing, A., Yuille, A., Urtasun, R.: · 2015
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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.H.S.: · 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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Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation
Papandreou, G., Chen, L.C., Murphy, K., Yuille, A.L.: · 2015
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The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2015
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The Cityscapes dataset
Cordts, M., Omran, M., Ramos, S., Scharwächter, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2015
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Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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Do deep nets really need to be deep?
Ba, L.J., Caruana, R.: · 2014
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Return of the devil in the details: Delving deep into convolutional networks
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
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Image geo-localization based on multiplenearest neighbor feature matching usinggeneralized graphs
Zamir, A.R., Shah, M.: · 2014
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Image geo-localization based on multiple nearest neighbor feature matching using generalized graphs
Zamir, A., Shah, M.: · 2014
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., Han, B.: · 2015
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The loss surfaces of multilayer networks
Choromanska, A., Henaff, M., Mathieu, M., Ben Arous, G., LeCun, Y.: · 2015
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Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: · 2015
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Han, S., Mao, H., Dally, W.J.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 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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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbaş, C., Golkov, V., Smagt, P.V., Cremers, D., Brox, T.: · 2015
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Understanding real world indoor scenes with synthetic data
Handa, A., Patraucean, V., Badrinarayanan, V., Stent, S., Cipolla, R.: · 2016
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SYNTHIA: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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SqueezeNet: AlexNet-level accuracy with 50 × \times fewer parameters and < < 1MB model size
Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: · 2016
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