-
1
-
-
84890543516
-
Advances in optimizing recurrent networks
-
IEEE
-
Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu. Advances in optimizing recurrent networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 8624-8628. IEEE, 2013.
-
(2013)
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
, pp. 8624-8628
-
-
Bengio, Y.1
Boulanger-Lewandowski, N.2
Pascanu, R.3
-
2
-
-
56749170578
-
Segmentation and recognition using structure from motion point clouds
-
G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla. Segmentation and recognition using structure from motion point clouds. In ECCV (1), pages 44-57, 2008.
-
(2008)
ECCV (1)
, pp. 44-57
-
-
Brostow, G.J.1
Shotton, J.2
Fauqueur, J.3
Cipolla, R.4
-
4
-
-
84959245343
-
Scene labeling with lstm recurrent neural networks
-
W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki. Scene labeling with lstm recurrent neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3547-3555, 2015.
-
(2015)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, pp. 3547-3555
-
-
Byeon, W.1
Breuel, T.M.2
Raue, F.3
Liwicki, M.4
-
5
-
-
85010197690
-
Joint calibration for semantic segmentation
-
H. Caesar, J. Uijlings, and V. Ferrari. Joint calibration for semantic segmentation. In BMVC, 2015.
-
(2015)
BMVC
-
-
Caesar, H.1
Uijlings, J.2
Ferrari, V.3
-
6
-
-
85198028989
-
Imagenet: A large-scale hierarchical image database
-
IEEE
-
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, pages 248-255. IEEE, 2009.
-
(2009)
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
, pp. 248-255
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.-J.4
Li, K.5
Fei-Fei, L.6
-
7
-
-
26444565569
-
Finding structure in time
-
J. L. Elman. Finding structure in time. Cognitive science, 14(2):179-211, 1990.
-
(1990)
Cognitive Science
, vol.14
, Issue.2
, pp. 179-211
-
-
Elman, J.L.1
-
8
-
-
84876258641
-
Learning hierarchical features for scene labeling
-
C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning hierarchical features for scene labeling. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8):1915-1929, 2013.
-
(2013)
Pattern Analysis and Machine Intelligence, IEEE Transactions on
, vol.35
, Issue.8
, pp. 1915-1929
-
-
Farabet, C.1
Couprie, C.2
Najman, L.3
LeCun, Y.4
-
9
-
-
84889591748
-
Offline Arabic handwriting recognition with multidimensional recurrent neural networks
-
Springer
-
A. Graves. Offline Arabic handwriting recognition with multidimensional recurrent neural networks. In Guide to OCR for Arabic Scripts, pages 297-313. Springer, 2012.
-
(2012)
Guide to OCR for Arabic Scripts
, pp. 297-313
-
-
Graves, A.1
-
10
-
-
84890543083
-
Speech recognition with deep recurrent neural networks
-
IEEE
-
A. Graves, A.-R. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 6645-6649. IEEE, 2013.
-
(2013)
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
, pp. 6645-6649
-
-
Graves, A.1
Mohamed, A.-R.2
Hinton, G.3
-
12
-
-
85162351107
-
Efficient inference in fully connected crfs with Gaussian edge potentials
-
P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with Gaussian edge potentials. In NIPS, 2011.
-
(2011)
NIPS
-
-
Krähenbühl, P.1
Koltun, V.2
-
14
-
-
78149356342
-
What, where and how many? Combining object detectors and crfs
-
Springer
-
L. Ladický, P. Sturgess, K. Alahari, C. Russell, and P. H. S. Torr. What, where and how many? combining object detectors and crfs. In Computer Vision-ECCV 2010, pages 424-437. Springer, 2010.
-
(2010)
Computer Vision-ECCV 2010
, pp. 424-437
-
-
Ladický, L.1
Sturgess, P.2
Alahari, K.3
Russell, C.4
Torr, P.H.S.5
-
15
-
-
70450169911
-
Nonparametric scene parsing: Label transfer via dense scene alignment
-
IEEE
-
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, pages 1972-1979. IEEE, 2009.
-
(2009)
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
, pp. 1972-1979
-
-
Liu, C.1
Yuen, J.2
Torralba, A.3
-
21
-
-
33745824267
-
Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation
-
Springer
-
J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Computer Vision-ECCV 2006, pages 1-15. Springer, 2006.
-
(2006)
Computer Vision-ECCV 2006
, pp. 1-15
-
-
Shotton, J.1
Winn, J.2
Rother, C.3
Criminisi, A.4
-
22
-
-
84937133497
-
Integrating parametric and non-parametric models for scene labeling
-
B. Shuai, G. Wang, Z. Zuo, B. Wang, and L. Zhao. Integrating parametric and non-parametric models for scene labeling. In Computer Vision and Pattern Recognition, 2015. CVPR 2015. IEEE Conference on. IEEE, 2015.
-
(2015)
Computer Vision and Pattern Recognition, 2015. CVPR 2015. IEEE Conference On. IEEE
-
-
Shuai, B.1
Wang, G.2
Zuo, Z.3
Wang, B.4
Zhao, L.5
-
24
-
-
84963815186
-
Scene parsing with integration of parametric and non-parametric models
-
B. Shuai, Z. Zuo, G.Wang, and B.Wang. Scene parsing with integration of parametric and non-parametric models. Image Processing, IEEE Transactions on, 2016.
-
(2016)
Image Processing, IEEE Transactions on
-
-
Shuai, B.1
Zuo, Z.2
Wang, G.3
Wang, B.4
-
28
-
-
78149311874
-
Superparsing: Scalable nonparametric image parsing with superpixels
-
Springer
-
J. Tighe and S. Lazebnik. Superparsing: scalable nonparametric image parsing with superpixels. In Computer Vision-ECCV 2010, pages 352-365. Springer, 2010.
-
(2010)
Computer Vision-ECCV 2010
, pp. 352-365
-
-
Tighe, J.1
Lazebnik, S.2
-
31
-
-
23744515371
-
A new class of upper bounds on the log partition function
-
M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky. A new class of upper bounds on the log partition function. Information Theory, IEEE Transactions on, 51(7):2313-2335, 2005.
-
(2005)
Information Theory, IEEE Transactions on
, vol.51
, Issue.7
, pp. 2313-2335
-
-
Wainwright, M.J.1
Jaakkola, T.S.2
Willsky, A.S.3
-
32
-
-
0025503558
-
Backpropagation through time: What it does and how to do it
-
P. J. Werbos. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10):1550-1560, 1990.
-
(1990)
Proceedings of the IEEE
, vol.78
, Issue.10
, pp. 1550-1560
-
-
Werbos, P.J.1
-
33
-
-
84911380286
-
Context driven scene parsing with attention to rare classes
-
IEEE
-
J. Yang, B. Price, S. Cohen, and M.-H. Yang. Context driven scene parsing with attention to rare classes. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 3294-3301. IEEE, 2014.
-
(2014)
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
, pp. 3294-3301
-
-
Yang, J.1
Price, B.2
Cohen, S.3
Yang, M.-H.4
-
35
-
-
78149338300
-
Semantic segmentation of urban scenes using dense depth maps
-
Springer
-
C. Zhang, L. Wang, and R. Yang. Semantic segmentation of urban scenes using dense depth maps. In Computer Vision-ECCV 2010, pages 708-721. Springer, 2010.
-
(2010)
Computer Vision-ECCV 2010
, pp. 708-721
-
-
Zhang, C.1
Wang, L.2
Yang, R.3
-
36
-
-
84937134364
-
-
arXiv preprint arXiv:1502.03240
-
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr. Conditional random fields as recurrent neural networks. arXiv preprint arXiv:1502.03240, 2015.
-
(2015)
Conditional Random Fields As Recurrent Neural Networks
-
-
Zheng, S.1
Jayasumana, S.2
Romera-Paredes, B.3
Vineet, V.4
Su, Z.5
Du, D.6
Huang, C.7
Torr, P.8
-
38
-
-
84937153918
-
Convolutional recurrent neural networks: Learning spatial dependencies for image representation
-
Z. Zuo, B. Shuai, G. Wang, X. Liu, X. Wang, B. Wang, and Y. Chen. Convolutional recurrent neural networks: Learning spatial dependencies for image representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 18-26, 2015.
-
(2015)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops
, pp. 18-26
-
-
Zuo, Z.1
Shuai, B.2
Wang, G.3
Liu, X.4
Wang, X.5
Wang, B.6
Chen, Y.7
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