-
2
-
-
84866688216
-
Measuring the objectness of image windows
-
B. Alexe, T. Deselaers, and V. Ferrari. Measuring the objectness of image windows. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34 (11): 2189-2202, 2012.
-
(2012)
Pattern Analysis and Machine Intelligence, IEEE Transactions on
, vol.34
, Issue.11
, pp. 2189-2202
-
-
Alexe, B.1
Deselaers, T.2
Ferrari, V.3
-
3
-
-
84959189820
-
Multiscale combinatorial grouping
-
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik. Multiscale combinatorial grouping. In IEEE CVPR, 2014.
-
(2014)
IEEE CVPR
-
-
Arbeláez, P.1
Pont-Tuset, J.2
Barron, J.T.3
Marques, F.4
Malik, J.5
-
4
-
-
84906309797
-
Bing: Binarized normed gradients for objectness estimation at 300fps
-
M. Cheng, Z. Zhang, W. Lin, and P. Torr. Bing: Binarized normed gradients for objectness estimation at 300fps. In IEEE CVPR, 2014.
-
(2014)
IEEE CVPR
-
-
Cheng, M.1
Zhang, Z.2
Lin, W.3
Torr, P.4
-
5
-
-
85044516828
-
Convolutional feature masking for joint object and stuff segmentation
-
J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In IEEE CVPR, 2015.
-
(2015)
IEEE CVPR
-
-
Dai, J.1
He, K.2
Sun, J.3
-
9
-
-
50949133669
-
Liblinear: A library for large linear classification
-
R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. Liblinear: A library for large linear classification. JMLR, 9, 2008.
-
(2008)
JMLR
, vol.9
-
-
Fan, R.1
Chang, K.2
Hsieh, C.3
Wang, X.4
Lin, C.5
-
10
-
-
77955422240
-
Object detection with discriminatively trained partbased models
-
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32 (9): 1627-1645, 2010.
-
(2010)
Pattern Analysis and Machine Intelligence, IEEE Transactions on
, vol.32
, Issue.9
, pp. 1627-1645
-
-
Felzenszwalb, P.F.1
Girshick, R.B.2
McAllester, D.3
Ramanan, D.4
-
11
-
-
9644254228
-
Efficient graph-based image segmentation
-
September
-
P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. Int. J. Comput. Vision, 59 (2): 167-181, September 2004.
-
(2004)
Int. J. Comput. Vision
, vol.59
, Issue.2
, pp. 167-181
-
-
Felzenszwalb, P.F.1
Huttenlocher, D.P.2
-
12
-
-
84940737052
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE CVPR, 2014.
-
(2014)
IEEE CVPR
-
-
Girshick, R.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
15
-
-
84913555165
-
-
arXiv preprint arXiv: 1408. 5093
-
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. ArXiv preprint arXiv: 1408. 5093, 2014.
-
(2014)
Caffe: Convolutional Architecture for Fast Feature Embedding
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
Guadarrama, S.7
Darrell, T.8
-
16
-
-
84906508364
-
Geodesic object proposals
-
P. Krähenbühl and V. Koltun. Geodesic object proposals. In ECCV 2014, pages 725-739. 2014.
-
(2014)
ECCV
, pp. 725-739
-
-
Krähenbühl, P.1
Koltun, V.2
-
17
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105. 2012.
-
(2012)
NIPS
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
18
-
-
84973861966
-
Deepbox: Learning objectness with convolutional networks
-
W. Kuo, B. Hariharan, and J. Malik. Deepbox: learning objectness with convolutional networks. In ICCV, 2015.
-
(2015)
ICCV
-
-
Kuo, W.1
Hariharan, B.2
Malik, J.3
-
19
-
-
84867135575
-
Building high-level features using large scale unsupervised learning
-
Q. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, and A. Ng. Building high-level features using large scale unsupervised learning. In ICML, 2012.
-
(2012)
ICML
-
-
Le, Q.1
Ranzato, M.2
Monga, R.3
Devin, M.4
Chen, K.5
Corrado, G.6
Dean, J.7
Ng, A.8
-
20
-
-
84977648895
-
What has my classifier learned. Visualizing the classification rules of bag-of-feature model by support region detection
-
L. Liu and L. Wang. What has my classifier learnedvisualizing the classification rules of bag-of-feature model by support region detection. In IEEE CVPR, 2012.
-
(2012)
IEEE CVPR
-
-
Liu, L.1
Wang, L.2
-
21
-
-
84898781017
-
Prime object proposals with randomized prim's algorithm
-
IEEE
-
S. Manen, M. Guillaumin, and L. Van Gool. Prime object proposals with randomized prim's algorithm. In IEEE ICCV, pages 2536-2543. IEEE, 2013.
-
(2013)
IEEE ICCV
, pp. 2536-2543
-
-
Manen, S.1
Guillaumin, M.2
Van Gool, L.3
-
22
-
-
84932160299
-
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
-
A. Nguyen, J. Yosinski, and J. Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In IEEE CVPR, 2015.
-
(2015)
IEEE CVPR
-
-
Nguyen, A.1
Yosinski, J.2
Clune, J.3
-
23
-
-
84977665662
-
Is object localization for free-weakly-supervised learning with convolutional neural networks
-
M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Is object localization for free-weakly-supervised learning with convolutional neural networks. In IEEE CVPR, 2015.
-
(2015)
IEEE CVPR
-
-
Oquab, M.1
Bottou, L.2
Laptev, I.3
Sivic, J.4
-
25
-
-
84856655925
-
Learning a category independent object detection cascade
-
E. Rahtu, J. Kannala, and M. Blaschko. Learning a category independent object detection cascade. In IEEE ICCV, pages 1052-1059, 2011.
-
(2011)
IEEE ICCV
, pp. 1052-1059
-
-
Rahtu, E.1
Kannala, J.2
Blaschko, M.3
-
26
-
-
84911429815
-
Generating object segmentation proposals using global and local search
-
IEEE
-
P. Rantalankila, J. Kannala, and E. Rahtu. Generating object segmentation proposals using global and local search. In IEEE CVPR, pages 2417-2424. IEEE, 2014.
-
(2014)
IEEE CVPR
, pp. 2417-2424
-
-
Rantalankila, P.1
Kannala, J.2
Rahtu, E.3
-
27
-
-
85083951635
-
Overfeat: Integrated recognition, localization and detection using convolutional networks
-
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
-
(2014)
ICLR
-
-
Sermanet, P.1
Eigen, D.2
Zhang, X.3
Mathieu, M.4
Fergus, R.5
LeCun, Y.6
-
28
-
-
85083953896
-
Deep inside convolutional networks: Visualising image classification models and saliency maps
-
K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In ICLR Workshop, 2014.
-
(2014)
ICLR Workshop
-
-
Simonyan, K.1
Vedaldi, A.2
Zisserman, A.3
-
29
-
-
84898989329
-
Deep neural networks for object detection
-
C. Szegedy, A. Toshev, and D. Erhan. Deep neural networks for object detection. In NIPS, pages 2553-2561, 2013.
-
(2013)
NIPS
, pp. 2553-2561
-
-
Szegedy, C.1
Toshev, A.2
Erhan, D.3
-
30
-
-
84881160857
-
Selective search for object recognition
-
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders. Selective search for object recognition. Int. J. Comput. Vision, 104 (2): 154-171, 2013.
-
(2013)
Int. J. Comput. Vision
, vol.104
, Issue.2
, pp. 154-171
-
-
Uijlings, J.R.R.1
De Van Sande, A.K.E.2
Gevers, T.3
Smeulders, A.W.M.4
-
31
-
-
84906334472
-
Weakly supervised object localization with latent category learning
-
C. Wang, W. Ren, K. Huang, and T. Tan. Weakly supervised object localization with latent category learning. In ECCV 2014, pages 431-445, 2014.
-
(2014)
ECCV 2014
, pp. 431-445
-
-
Wang, C.1
Ren, W.2
Huang, K.3
Tan, T.4
-
32
-
-
84977634551
-
Object proposal by multi-branch hierarchical segmentation
-
C. Wang, L. Zhao, S. Liang, L. Zhang, J. Jia, and Y. Wei. Object proposal by multi-branch hierarchical segmentation. In IEEE CVPR, 2015.
-
(2015)
IEEE CVPR
-
-
Wang, C.1
Zhao, L.2
Liang, S.3
Zhang, L.4
Jia, J.5
Wei, Y.6
-
33
-
-
84937902251
-
Visualizing and understanding convolutional neural networks
-
M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. In ECCV, 2014.
-
(2014)
ECCV
-
-
Zeiler, M.D.1
Fergus, R.2
-
34
-
-
84911443783
-
Panda: Pose aligned networks for deep attribute modeling
-
N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. Panda: Pose aligned networks for deep attribute modeling. In IEEE CVPR, pages 1637-1644, 2014.
-
(2014)
IEEE CVPR
, pp. 1637-1644
-
-
Zhang, N.1
Paluri, M.2
Ranzato, M.3
Darrell, T.4
Bourdev, L.5
-
35
-
-
85083952996
-
Object detectors emerge in deep scene cnns
-
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Object detectors emerge in deep scene cnns. In ICLR, 2015.
-
(2015)
ICLR
-
-
Zhou, B.1
Khosla, A.2
Lapedriza, A.3
Oliva, A.4
Torralba, A.5
-
36
-
-
84906489617
-
Edge boxes: Locating object proposals from edges
-
C L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In ECCV 2014, pages 391-405. 2014.
-
(2014)
ECCV 2014
, pp. 391-405
-
-
Zitnick, C.L.1
Dollár, P.2
|