-
1
-
-
84973914286
-
Pooling in image representation: The visual codeword point of view
-
2
-
S. Avila, N. Thome, M. Cord, E. Valle, and A. Araujo. Pooling in image representation: the visual codeword point of view. Computer Vision and Image Understanding, 2012. 2
-
(2012)
Computer Vision and Image Understanding
-
-
Avila, S.1
Thome, N.2
Cord, M.3
Valle, E.4
Araujo, A.5
-
2
-
-
84911427064
-
Optimizing average precision using weakly supervised data
-
2, 4, 5, 7, 8
-
A. Behl, C. V. Jawahar, and M. P. Kumar. Optimizing average precision using weakly supervised data. In CVPR, 2014. 2, 4, 5, 7, 8
-
(2014)
CVPR
-
-
Behl, A.1
Jawahar, C.V.2
Kumar, M.P.3
-
3
-
-
84911382755
-
Object classification with latent window parameters
-
1, 2
-
H. Bilen, V. Namboodiri, and L. Van Gool. Object classification with latent window parameters. In IJCV, 2013. 1, 2
-
(2013)
IJCV
-
-
Bilen, H.1
Namboodiri, V.2
Van Gool, L.3
-
4
-
-
84973892991
-
Tutorial: Visual learning with weak supervision
-
1
-
M. Blaschko, P. Kumar, and B. Taskar. Tutorial: Visual learning with weak supervision, CVPR 2013. 1
-
(2013)
CVPR
-
-
Blaschko, M.1
Kumar, P.2
Taskar, B.3
-
5
-
-
85072028231
-
Return of the devil in the details: Delving deep into convolutional nets
-
2, 8
-
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In BMVC, 2014. 2, 8
-
(2014)
BMVC
-
-
Chatfield, K.1
Simonyan, K.2
Vedaldi, A.3
Zisserman, A.4
-
6
-
-
84873463794
-
Regularized bundle methods for convex and non-convex risks
-
4
-
T.-M.-T. Do and T. Artières. Regularized bundle methods for convex and non-convex risks. JMLR, 2012. 4
-
(2012)
JMLR
-
-
Do, T.-M.-T.1
Artières, T.2
-
7
-
-
84898936638
-
Mid-level visual element discovery as discriminative mode seeking
-
6
-
C. Doersch, A. Gupta, and A. A. Efros. Mid-level visual element discovery as discriminative mode seeking. In NIPS, 2013. 6
-
(2013)
NIPS
-
-
Doersch, C.1
Gupta, A.2
Efros, A.A.3
-
8
-
-
84949926853
-
Incremental learning of latent structural SVM for weakly supervised image classification
-
1
-
T. Durand, N. Thome, M. Cord, and D. Picard. Incremental learning of latent structural SVM for weakly supervised image classification. In ICIP, 2014. 1
-
(2014)
ICIP
-
-
Durand, T.1
Thome, N.2
Cord, M.3
Picard, D.4
-
9
-
-
77955422240
-
Object detection with discriminatively trained part based models
-
1, 2, 7
-
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 2010. 1, 2, 7
-
(2010)
PAMI
-
-
Felzenszwalb, P.F.1
Girshick, R.B.2
McAllester, D.3
Ramanan, D.4
-
10
-
-
84911400494
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
1
-
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1
-
(2014)
CVPR
-
-
Girshick, R.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
12
-
-
84938217896
-
Multi-scale orderless pooling of deep convolutional activation features
-
2, 6
-
Y. Gong, L. Wang, R. Guo, and S. Lazebnik. Multi-scale orderless pooling of deep convolutional activation features. In ECCV, 2014. 2, 6
-
(2014)
ECCV
-
-
Gong, Y.1
Wang, L.2
Guo, R.3
Lazebnik, S.4
-
13
-
-
84928278589
-
Spatial pyramid pooling in deep convolutional networks for visual recognition
-
2, 8
-
K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014. 2, 8
-
(2014)
ECCV
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
14
-
-
84913580146
-
Caffe: Convolutional architecture for fast feature embedding
-
5, 6
-
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In ACM International Conference on Multimedia, 2014. 5, 6
-
(2014)
ACM International Conference on Multimedia
-
-
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
-
-
84887325186
-
Blocks that shout: Distinctive parts for scene classification
-
2, 5, 6
-
M. Juneja, A. Vedaldi, C. V. Jawahar, and A. Zisserman. Blocks that shout: Distinctive parts for scene classification. In CVPR, 2013. 2, 5, 6
-
(2013)
CVPR
-
-
Juneja, M.1
Vedaldi, A.2
Jawahar, C.V.3
Zisserman, A.4
-
17
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
1, 2
-
A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS. 2012. 1, 2
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.3
-
18
-
-
85161967298
-
Self-paced learning for latent variable models
-
1, 2
-
P. Kumar, B. Packer, and D. Koller. Self-paced learning for latent variable models. In NIPS, 2010. 1, 2
-
(2010)
NIPS
-
-
Kumar, P.1
Packer, B.2
Koller, D.3
-
19
-
-
84911453134
-
Fantope regularization in metric learning
-
8
-
M. T. Law, N. Thome, and M. Cord. Fantope regularization in metric learning. In CVPR, 2014. 8
-
(2014)
CVPR
-
-
Law, M.T.1
Thome, N.2
Cord, M.3
-
20
-
-
33845572523
-
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
-
2, 5, 6
-
S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006. 2, 5, 6
-
(2006)
CVPR
-
-
Lazebnik, S.1
Schmid, C.2
Ponce, J.3
-
21
-
-
50649103674
-
What, where and who? Classifying events by scene and object recognition
-
5
-
L.-J. Li and F.-F. Li. What, where and who? classifying events by scene and object recognition. In ICCV, 2007. 5
-
(2007)
ICCV
-
-
Li, L.-J.1
Li, F.-F.2
-
22
-
-
85162513516
-
Object bank: A high-level image representation for scene classification & semantic feature sparsification
-
5, 6, 8
-
E. P. X. Li-Jia Li, Hao Su and L. Fei-Fei. Object bank: A high-level image representation for scene classification & semantic feature sparsification. In NIPS, 2010. 5, 6, 8
-
(2010)
NIPS
-
-
Li-Jia Li, E.P.X.1
Su, H.2
Fei-Fei, L.3
-
23
-
-
84937889162
-
Efficient optimization for average precision SVM
-
5
-
P. Mohapatra, C. Jawahar, and M. P. Kumar. Efficient optimization for average precision SVM. In NIPS. 2014. 5
-
(2014)
NIPS
-
-
Mohapatra, P.1
Jawahar, C.2
Kumar, M.P.3
-
24
-
-
84911449395
-
Learning and transferring mid-level image representations using convolutional neural networks
-
1, 8
-
M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In CVPR, 2014. 1, 8
-
(2014)
CVPR
-
-
Oquab, M.1
Bottou, L.2
Laptev, I.3
Sivic, J.4
-
25
-
-
84856650974
-
Scene recognition and weakly supervised object localization with deformable part-based models
-
1, 2
-
M. Pandey and S. Lazebnik. Scene recognition and weakly supervised object localization with deformable part-based models. In ICCV, 2011. 1, 2
-
(2011)
ICCV
-
-
Pandey, M.1
Lazebnik, S.2
-
26
-
-
84866674135
-
Reconfigurable models for scene recognition
-
1, 2, 6
-
S. N. Parizi, J. G. Oberlin, and P. F. Felzenszwalb. Reconfigurable models for scene recognition. In CVPR, 2012. 1, 2, 6
-
(2012)
CVPR
-
-
Parizi, S.N.1
Oberlin, J.G.2
Felzenszwalb, P.F.3
-
27
-
-
34948815101
-
Fisher kernels on visual vocabularies for image categorization
-
2
-
F. Perronnin and C. R. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007. 2
-
(2007)
CVPR
-
-
Perronnin, F.1
Dance, C.R.2
-
28
-
-
70450162315
-
Recognizing indoor scenes
-
5
-
A. Quattoni and A. Torralba. Recognizing indoor scenes. In CVPR, 2009. 5
-
(2009)
CVPR
-
-
Quattoni, A.1
Torralba, A.2
-
29
-
-
84885881090
-
Objectcentric spatial pooling for image classification
-
1, 2
-
O. Russakovsky, Y. Lin, K. Yu, and L. Fei-Fei. Objectcentric spatial pooling for image classification. In ECCV, 2012. 1, 2
-
(2012)
ECCV
-
-
Russakovsky, O.1
Lin, Y.2
Yu, K.3
Fei-Fei, L.4
-
30
-
-
84902249208
-
Latent pyramidal regions for recognizing scenes
-
2, 5, 6
-
F. Sadeghi and M. F. Tappen. Latent pyramidal regions for recognizing scenes. In ECCV, 2012. 2, 5, 6
-
(2012)
ECCV
-
-
Sadeghi, F.1
Tappen, M.F.2
-
31
-
-
33847380121
-
Robust object recognition with cortex-like mechanisms
-
2
-
T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio. Robust object recognition with cortex-like mechanisms. PAMI, 2007. 2
-
(2007)
PAMI
-
-
Serre, T.1
Wolf, L.2
Bileschi, S.3
Riesenhuber, M.4
Poggio, T.5
-
32
-
-
84866677469
-
Discriminative spatial saliency for image classification
-
1, 6
-
G. Sharma, F. Jurie, and C. Schmid. Discriminative spatial saliency for image classification. In CVPR, 2012. 1, 6
-
(2012)
CVPR
-
-
Sharma, G.1
Jurie, F.2
Schmid, C.3
-
33
-
-
84898806407
-
Learning discriminative part detectors for image classification and cosegmentation
-
1, 2, 5, 6
-
J. Sun and J. Ponce. Learning discriminative part detectors for image classification and cosegmentation. In ICCV, 2013. 1, 2, 5, 6
-
(2013)
ICCV
-
-
Sun, J.1
Ponce, J.2
-
34
-
-
84887333346
-
Dynamic scene classification: Learning motion descriptors with slow features analysis
-
2
-
C. Thériault, N. Thome, and M. Cord. Dynamic scene classification: Learning motion descriptors with slow features analysis. In CVPR, 2013. 2
-
(2013)
CVPR
-
-
Thériault, C.1
Thome, N.2
Cord, M.3
-
36
-
-
24944537843
-
Large margin methods for structured and interdependent output variables
-
3
-
I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 2005. 3
-
(2005)
JMLR
-
-
Tsochantaridis, I.1
Joachims, T.2
Hofmann, T.3
Altun, Y.4
-
37
-
-
77955987964
-
Grouplet: A structured image representation for recognizing human and object interactions
-
5
-
B. Yao and L. Fei-Fei. Grouplet: A structured image representation for recognizing human and object interactions. In CVPR, 2010. 5
-
(2010)
CVPR
-
-
Yao, B.1
Fei-Fei, L.2
-
38
-
-
71149086466
-
Learning structural SVMs with latent variables
-
1, 2, 5, 6, 7
-
C.-N. Yu and T. Joachims. Learning structural SVMs with latent variables. In ICML, 2009. 1, 2, 5, 6, 7
-
(2009)
ICML
-
-
Yu, C.-N.1
Joachims, T.2
-
39
-
-
36448983903
-
A support vector method for optimizing average precision
-
2, 4, 5
-
Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR, 2007. 2, 4, 5
-
(2007)
SIGIR
-
-
Yue, Y.1
Finley, T.2
Radlinski, F.3
Joachims, T.4
-
41
-
-
84911443783
-
PANDA: Pose aligned networks for deep attribute modeling
-
2, 6
-
N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. PANDA: Pose Aligned Networks for Deep Attribute Modeling. In ECCV, 2014. 2, 6
-
(2014)
ECCV
-
-
Zhang, N.1
Paluri, M.2
Ranzato, M.3
Darrell, T.4
Bourdev, L.5
-
42
-
-
84937964578
-
Learning deep features for scene recognition using places database
-
2, 5, 6
-
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. NIPS, 2014. 2, 5, 6
-
(2014)
NIPS
-
-
Zhou, B.1
Lapedriza, A.2
Xiao, J.3
Torralba, A.4
Oliva, A.5
|