Fetching the paper…
Reading the bibliography…
Pixel-wise semantic segmentation for visual scene understanding not only needs to be accurate, but also efficient in order to find any use in real-time application.
Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , pp. 255–258, 1998
1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, G. B. Orr, and K. R. Müller, Neural Networks: Tricks of the Trade . Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, ch. Efficient BackProp, pp. 9–50
1998
Earlier work this paper cites.
M. A. Ranzato, F. J. Huang, Y.-L. Boureau, and Y. LeCun, “Unsupervised learning of invariant feature hierarchies with applications to object recognition,” in Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on , 2007, pp. 1–8
2007
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 ECCV (1) , 2008, pp. 44–57
2008
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 , 2009
2009
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in Proceedings of the 28th international conference on machine learning (ICML-11) , 2011, pp. 689–696
2011
Earlier work this paper cites.
R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A matlab-like environment for machine learning,” in BigLearn, NIPS Workshop , 2011
2011
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 25 , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
X. Ren, L. Bo, and D. Fox, “Rgb-(d) scene labeling: Features and algorithms,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on , 2012, pp. 2759–2766
2012
Earlier work this paper cites.
2013
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, Aug 2013
2013
Earlier work this paper cites.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1–9
2015
Cited alongside, same era.
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 Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1529–1537
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…
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, “Efficient object localization using convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 648–656
2015
Cited alongside, same era.
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 , 2015, pp. 2650–2658
2015
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Cited alongside, same era.
2015
Cited alongside, same era.
H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1520–1528
2015
Cited alongside, same era.
2015
Cited alongside, same era.
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 , 2015, pp. 3431–3440
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2016
Later among the works it cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 779–788
2016
Later among the works it cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European Conference on Computer Vision . Springer, 2016, pp. 21–37
2016
Later among the works it cites.
2016
Later among the works it cites.
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 Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2016, pp. 41–48
2016
Later among the works it cites.