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Semantic segmentation has achieved remarkable progress but remains challenging due to the complex scene, object occlusion, and so on.
G. Lin, A. Milan, C. Shen, and I. Reid, “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Honolulu, HI, Jul. 2017, pp. 1925–1934
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V. Vapnik and A. Vashist, “A new learning paradigm: Learning using privileged information,” Neural Networks , vol. 22, no. 5, pp. 544–557, 2009
2009
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Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th annual international conference on machine learning , Montreal, Canada, Jun. 2009, pp. 41–48
2009
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M. P. Kumar, B. Packer, and D. Koller, “Self-paced learning for latent variable models,” in Advances in Neural Information Processing Systems , Vancouver, Canada, Dec. 2010, pp. 1189–1197
2010
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C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1915–1929, 2012
2012
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J. Feyereisl and U. Aickelin, “Privileged information for data clustering,” Information Sciences , vol. 194, pp. 4–23, 2012
2012
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N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in European conference on computer vision , Firenze, Italy, Oct. 2012, pp. 746–760
2012
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S. Fouad, P. Tino, S. Raychaudhury, and P. Schneider, “Incorporating privileged information through metric learning,” IEEE transactions on neural networks and learning systems , vol. 24, no. 7, pp. 1086–1098, 2013
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S. Fouad and P. Tiňo, “Ordinal-based metric learning for learning using privileged information,” in International Joint Conference on Neural Networks , State of Texas, US, Aug. 2013
2013
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V. Sharmanska, N. Quadrianto, and C. H. Lampert, “Learning to rank using privileged information,” in IEEE International Conference on Computer Vision , Sydney, Australia, Dec. 2013
2013
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H. Yang and I. Patras, “Privileged information-based conditional regression forest for facial feature detection,” in 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) , Shanghai, China, Apr. 2013, pp. 1–6
2013
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S. Gupta, P. Arbelaez, and J. Malik, “Perceptual organization and recognition of indoor scenes from rgb-d images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Portland, OR, Jun. 2013, pp. 564–571
2013
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W. Li, L. Niu, and D. Xu, “Exploiting privileged information from web data for image categorization,” in European conference on computer vision , Beijing,China, Jun. 2014
2014
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M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich, “Feedforward semantic segmentation with zoom-out features,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Boston, MA, Jun. 2015
2015
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J. Dai, K. He, and J. Sun, “Convolutional feature masking for joint object and stuff segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Boston, MA, Jun. 2015
2015
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Z. Liu, X. Li, P. Luo, C.-C. Loy, and X. Tang, “Semantic image segmentation via deep parsing network,” in IEEE International Conference on Computer Vision , Santiago, Chile, Dec. 2015
2015
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S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr, “Conditional random fields as recurrent neural networks,” in IEEE International Conference on Computer Vision , Santiago, Chile, Dec. 2015
2015
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S. Gupta, P. Arbeláez, R. Girshick, and J. Malik, “Indoor scene understanding with rgb-d images: Bottom-up segmentation, object detection and semantic segmentation,” International Journal of Computer Vision , vol. 112, no. 2, pp. 133–149, 2015
2015
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X. Xu, W. Li, and D. Xu, “Distance metric learning using privileged information for face verification and person re-identification,” IEEE transactions on neural networks and learning systems , vol. 26, no. 12, pp. 3150–3162, 2015
2015
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Boston, MA, Jun. 2015, pp. 3431–3440
2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention , Munich, Germany, Oct. 2015, pp. 234–241
2015
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D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” in Proceedings of the IEEE international conference on computer vision , Santiago, Chile, Dec. 2015, pp. 2650–2658
2015
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P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. L. Yuille, “Towards unified depth and semantic prediction from a single image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Boston, MA, Jun. 2015, pp. 2800–2809
2015
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A. Pentina, V. Sharmanska, and C. H. Lampert, “Curriculum learning of multiple tasks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Boston, MA, Jun. 2015
2015
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S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Boston, MA, Jun. 2015, pp. 567–576
2015
Cited alongside, same era.
V. Jampani, M. Kiefel, and P. V. Gehler, “Learning sparse high dimensional filters: Image filtering, dense CRFs and bilateral neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, NV, Jun. 2016
2016
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R. Vemulapalli, O. Tuzel, M.-Y. Liu, and R. Chellapa, “Gaussian conditional random field network for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, NV, Jun. 2016
2016
Y. Zhang, P. David, and B. Gong, “Curriculum domain adaptation for semantic segmentation of urban scenes,” in IEEE International Conference on Computer Vision , Venice, Italy, Oct. 2017
2017
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W. Lotter, G. Sorensen, and D. Cox, “A multi-scale cnn and curriculum learning strategy for mammogram classification,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , 2017, pp. 169–177
2017
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2017
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A. Graves, M. G. Bellemare, J. Menick, R. Munos, and K. Kavukcuoglu, “Automated curriculum learning for neural networks,” in Proceedings of the 34th International Conference on Machine Learning , Sydney, Australia, Aug. 2017, pp. 1311–1320
2017
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Cited alongside, same era.
S. Chandra and I. Kokkinos, “Fast, exact and multi-scale inference for semantic image segmentation with deep gaussian CRFs,” in European conference on computer vision , Amsterdam, The Netherlands, Oct. 2016
2016
Cited alongside, same era.
J. Wang, Z. Wang, D. Tao, S. See, and G. Wang, “Learning common and specific features for rgb-d semantic segmentation with deconvolutional networks,” in European Conference on Computer Vision , Amsterdam, The Netherlands, Oct. 2016, pp. 664–679
2016
Cited alongside, same era.
C. Hazirbas, L. Ma, C. Domokos, and D. Cremers, “Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture,” in Asian conference on computer vision , Taipei, Nov. 2016, pp. 213–228
2016
Cited alongside, same era.
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, NV, Jun. 2016
2016
Cited alongside, same era.
W. Wang and J. Shen, “Higher-order image co-segmentation,” IEEE Transactions on Multimedia , vol. 18, no. 6, pp. 1011–1021, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
J. Hoffman, S. Gupta, and T. Darrell, “Learning with side information through modality hallucination,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Las Vegas, NV, Jun. 2016, pp. 826–834
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and S. Jian, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, NV, Jun. 2016
2016
Cited alongside, same era.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2018
2018
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J. Liu, Y. Wang, Y. Li, J. Fu, J. Li, and H. Lu, “Collaborative deconvolutional neural networks for joint depth estimation and semantic segmentation,” IEEE transactions on neural networks and learning systems , vol. 29, no. 11, pp. 5655–5666, 2018
2018
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B. Kang, Y. Lee, and T. Q. Nguyen, “Depth-adaptive deep neural network for semantic segmentation,” IEEE Transactions on Multimedia , vol. 20, no. 9, pp. 2478–2490, 2018
2018
Later among the works it cites.
V. Nekrasov, C. Shen, and I. Reid, “Light-weight refinenet for real-time semantic segmentation,” 2018
2018
Later among the works it cites.
Z. Zhang, Z. Cui, C. Xu, Z. Jie, X. Li, and J. Yang, “Joint task-recursive learning for semantic segmentation and depth estimation,” in European conference on computer vision , Munich,Germany, Sep. 2018
2018
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H. Ding, X. Jiang, B. Shuai, A. Qun Liu, and G. Wang, “Context contrasted feature and gated multi-scale aggregation for scene segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Salt Lake City, UT, Jun. 2018
2018
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F. Zhou, Y. Hu, and X. Shen, “Mfdcnn: A multimodal fusion dcnn framework for object detection and segmentation,” in Pacific Rim Conference on Multimedia , Hefei, China, Sep. 2018
2018
Later among the works it cites.
W. Wang and U. Neumann, “Depth-aware CNN for RGB-D segmentation,” in European conference on computer vision , Munich,Germany, Sep. 2018
2018
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J. Jiao, Y. Cao, Y. Song, and R. Lau, “Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss,” in European conference on computer vision , Munich,Germany, Sep. 2018
2018
Later among the works it cites.
J. Lambert, O. Sener, and S. Savarese, “Deep learning under privileged information using heteroscedastic dropout,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Salt Lake City, UT, Jun. 2018
2018
Later among the works it cites.
Q. Wang, C. Yuan, and Y. Liu, “Learning deep conditional neural network for image segmentation,” IEEE Transactions on Multimedia , vol. 21, no. 7, pp. 1839–1852, 2019
2019
Closest in time.
J. Li, L. Wei, F. Zhang, T. Yang, and Z. Lu, “Joint deep and depth for object-level segmentation and stereo tracking in crowds,” IEEE Transactions on Multimedia , vol. 21, no. 10, pp. 2531–2544, 2019
2019
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2019
Closest in time.
Z. Zhang, Z. Cui, C. Xu, Y. Yan, N. Sebe, and J. Yang, “Pattern-affinitive propagation across depth, surface normal and semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Long Beach, CA, Jun. 2019
2019
Closest in time.
Y. Chen, W. Li, X. Chen, and L. V. Gool, “Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Long Beach, CA, Jun. 2019
2019
Closest in time.
K.-H. Lee, G. Ros, J. Li, and A. Gaidon, “SPIGAN: Privileged adversarial learning from simulation,” May. 2019
2019
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2019
Closest in time.
V. Nekrasov, T. Dharmasiri, A. Spek, T. Drummond, C. Shen, and I. Reid, “Real-time joint semantic segmentation and depth estimation using asymmetric annotations,” in International Conference on Robotics and Automation , Montreal, Canada, May. 2019, pp. 7101–7107
2019
Closest in time.