Fetching the paper…
Reading the bibliography…
Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers.
C. Liu, J. Yuen, and A. Torralba, “Nonparametric scene parsing: Label transfer via dense scene alignment,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 1972–1979
1979
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
P. O. Pinheiro, R. Collobert, and P. Dollar, “Learning to segment object candidates,” in Advances in Neural Information Processing Systems , 2015, pp. 1990–1998
1998
Earlier work this paper cites.
Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on pattern analysis and machine intelligence , vol. 23, no. 11, pp. 1222–1239, 2001
2001
Earlier work this paper cites.
C. Rother, V. Kolmogorov, and A. Blake, “Grabcut: Interactive foreground extraction using iterated graph cuts,” in ACM transactions on graphics (TOG) , vol. 23, no. 3. ACM, 2004, pp. 309–314
2004
Earlier work this paper cites.
F. Ning, D. Delhomme, Y. LeCun, F. Piano, L. Bottou, and P. E. Barbano, “Toward automatic phenotyping of developing embryos from videos,” IEEE Transactions on Image Processing , vol. 14, no. 9, pp. 1360–1371, 2005
2005
Earlier work this paper cites.
A. Ahmed, K. Yu, W. Xu, Y. Gong, and E. Xing, “Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks,” in European Conference on Computer Vision . Springer, 2008, pp. 69–82
2008
Earlier work this paper cites.
G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, “Segmentation and recognition using structure from motion point clouds,” in European Conference on Computer Vision . Springer, 2008, pp. 44–57
2008
Earlier work this paper cites.
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, “Labelme: a database and web-based tool for image annotation,” International journal of computer vision , vol. 77, no. 1, pp. 157–173, 2008
2008
Earlier work this paper cites.
A. Ess, T. Müller, H. Grabner, and L. J. Van Gool, “Segmentation-based urban traffic scene understanding.” in BMVC , vol. 1, 2009, p. 2
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 248–255
2009
Earlier work this paper cites.
G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” Pattern Recognition Letters , vol. 30, no. 2, pp. 88–97, 2009
2009
Earlier work this paper cites.
P. Sturgess, K. Alahari, L. Ladicky, and P. H. Torr, “Combining appearance and structure from motion features for road scene understanding,” in BMVC 2012-23rd British Machine Vision Conference . BMVA, 2009
2009
Earlier work this paper cites.
S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” in Computer Vision, 2009 IEEE 12th International Conference on . IEEE, 2009, pp. 1–8
2009
Earlier work this paper cites.
X. Chen, A. Golovinskiy, and T. Funkhouser, “A benchmark for 3D mesh segmentation,” ACM Transactions on Graphics (Proc. SIGGRAPH) , vol. 28, no. 3, Aug. 2009
2009
Earlier work this paper cites.
J. Shotton, J. Winn, C. Rother, and A. Criminisi, “Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context,” International Journal of Computer Vision , vol. 81, no. 1, pp. 2–23, 2009
2009
Earlier work this paper cites.
2011
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik, “Semantic contours from inverse detectors,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 991–998
2011
Earlier work this paper cites.
K. Lai, L. Bo, X. Ren, and D. Fox, “A large-scale hierarchical multi-view rgb-d object dataset,” in Robotics and Automation (ICRA), 2011 IEEE International Conference on . IEEE, 2011, pp. 1817–1824
2011
Earlier work this paper cites.
V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” Adv. Neural Inf. Process. Syst , vol. 2, no. 3, p. 4, 2011
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition , June 2012, pp. 3354–3361
2012
Earlier work this paper cites.
D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in neural information processing systems , 2012, pp. 2843–2851
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
J. M. Alvarez, T. Gevers, Y. LeCun, and A. M. Lopez, “Road scene segmentation from a single image,” in European Conference on Computer Vision . Springer, 2012, pp. 376–389
2012
Earlier work this paper cites.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in European Conference on Computer Vision . Springer, 2012, pp. 746–760
2012
Earlier work this paper cites.
A. Quadros, J. Underwood, and B. Douillard, “An occlusion-aware feature for range images,” in Robotics and Automation, 2012. ICRA’12. IEEE International Conference on . IEEE, May 14-18 2012
2012
Earlier work this paper cites.
A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari, “Learning object class detectors from weakly annotated video,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on . IEEE, 2012, pp. 3282–3289
2012
Earlier work this paper cites.
A. Richtsfeld, “The object segmentation database (osd),” 2012
2012
Earlier work this paper cites.
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, 2013
2013
Earlier work this paper cites.
J. Xiao, A. Owens, and A. Torralba, “Sun3d: A database of big spaces reconstructed using sfm and object labels,” in 2013 IEEE International Conference on Computer Vision , Dec 2013, pp. 1625–1632
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
S. Bell, P. Upchurch, N. Snavely, and K. Bala, “OpenSurfaces: A richly annotated catalog of surface appearance,” ACM Trans. on Graphics (SIGGRAPH) , vol. 32, no. 4, 2013
2013
Earlier work this paper cites.
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 , 2013, pp. 564–571
2013
Earlier work this paper cites.
A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell, A Category-Level 3D Object Dataset: Putting the Kinect to Work . London: Springer London, 2013, pp. 141–165. [Online]. Available: http://dx.doi.org/10.1007/978-1-4471-4640-7_8
2013
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Parameter learning and convergent inference for dense random fields.” in ICML (3) , 2013, pp. 513–521
2013
Earlier work this paper cites.
J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, “Deep learning for content-based image retrieval: A comprehensive study,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 157–166
2014
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Simultaneous detection and segmentation,” in European Conference on Computer Vision . Springer, 2014, pp. 297–312
2014
Earlier work this paper cites.
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, “Learning rich features from rgb-d images for object detection and segmentation,” in European Conference on Computer Vision . Springer, 2014, pp. 345–360
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 1717–1724
2014
Cited alongside, same era.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
Cited alongside, same era.
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille, “The role of context for object detection and semantic segmentation in the wild,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Cited alongside, same era.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 4489–4497
2015
Later among the works it cites.
2015
Later among the works it cites.
2015
Later among the works it cites.
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille, “Detect what you can: Detecting and representing objects using holistic models and body parts,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Cited alongside, same era.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision . Springer, 2014, pp. 740–755
2014
Cited alongside, same era.
S. D. Jain and K. Grauman, “Supervoxel-consistent foreground propagation in video,” in European Conference on Computer Vision . Springer, 2014, pp. 656–671
2014
Cited alongside, same era.
2014
Cited alongside, same era.
P. H. Pinheiro and R. Collobert, “Recurrent convolutional neural networks for scene labeling.” in ICML , 2014, pp. 82–90
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision . Springer, 2014, pp. 818–833
2014
Cited alongside, same era.
2014
Cited alongside, same era.
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik, “Multiscale combinatorial grouping,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 328–335
2014
Cited alongside, same era.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3213–3223
2016
Later among the works it cites.
H. Zhu, F. Meng, J. Cai, and S. Lu, “Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation,” Journal of Visual Communication and Image Representation , vol. 34, pp. 12 – 27, 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1047320315002035
2016
Later among the works it cites.
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Later among the works it cites.
2016
Later among the works it cites.
X. Shen, A. Hertzmann, J. Jia, S. Paris, B. Price, E. Shechtman, and I. Sachs, “Automatic portrait segmentation for image stylization,” in Computer Graphics Forum , vol. 35, no. 2. Wiley Online Library, 2016, pp. 93–102
2016
Later among the works it cites.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3234–3243
2016
Later among the works it cites.
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” in Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
L. Yi, V. G. Kim, D. Ceylan, I.-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. Guibas, “A scalable active framework for region annotation in 3d shape collections,” SIGGRAPH Asia , 2016
2016
Later among the works it cites.
T. Hackel, J. D. Wegner, and K. Schindler, “Contour detection in unstructured 3d point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1610–1618
2016
Later among the works it cites.
I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese, “3d semantic parsing of large-scale indoor spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1534–1543
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
A. Roy and S. Todorovic, “A multi-scale cnn for affordance segmentation in rgb images,” in European Conference on Computer Vision . Springer, 2016, pp. 186–201
2016
Later among the works it cites.
X. Bian, S. N. Lim, and N. Zhou, “Multiscale fully convolutional network with application to industrial inspection,” in Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on . IEEE, 2016, pp. 1–8
2016
Later among the works it cites.
F. Visin, M. Ciccone, A. Romero, K. Kastner, K. Cho, Y. Bengio, M. Matteucci, and A. Courville, “Reseg: A recurrent neural network-based model for semantic segmentation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2016
2016
Later among the works it cites.
Z. Li, Y. Gan, X. Liang, Y. Yu, H. Cheng, and L. Lin, LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling . Cham: Springer International Publishing, 2016, pp. 541–557. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-46475-6_34
2016
Later among the works it cites.
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár, “Learning to refine object segments,” in European Conference on Computer Vision . Springer, 2016, pp. 75–91
2016
Later among the works it cites.
2016
Later among the works it cites.
J. Huang and S. You, “Point cloud labeling using 3d convolutional neural network,” in Proc. of the International Conf. on Pattern Recognition (ICPR) , vol. 2, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
E. Shelhamer, K. Rakelly, J. Hoffman, and T. Darrell, “Clockwork convnets for video semantic segmentation,” in Computer Vision–ECCV 2016 Workshops . Springer, 2016, pp. 852–868
2016
Later among the works it cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Deep end2end voxel2voxel prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2016, pp. 17–24
2016
Later among the works it cites.
2016
Later among the works it cites.
G. Li and Y. Yu, “Deep contrast learning for salient object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 478–487
2016
Later among the works it cites.
2016
Later among the works it cites.
C. Hazirbas, L. Ma, C. Domokos, and D. Cremers, “Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture,” in Proc. ACCV , vol. 2, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in Proceedings of the 33rd annual international conference on machine learning. ACM , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
2017
Closest in time.
I. Armeni, A. Sax, A. R. Zamir, and S. Savarese, “Joint 2D-3D-Semantic Data for Indoor Scene Understanding,” ArXiv e-prints , Feb. 2017
2017
Closest in time.
2017
Closest in time.
M. D. Zeiler, G. W. Taylor, and R. Fergus, “Adaptive deconvolutional networks for mid and high level feature learning,” in Computer Vision (ICCV), 2011 IEEE International Conference on . IEEE, 2011, pp. 2018–2025
2025
Closest in time.