-
1
-
-
77049086558
-
On seeing stuff: The perception of materials by humans and machines
-
2
-
E. H. Adelson. On seeing stuff: The perception of materials by humans and machines. In Photonics West 2001-Electronic Imaging, 2001. 2
-
(2001)
Photonics West 2001-Electronic Imaging
-
-
Adelson, E.H.1
-
2
-
-
84973389608
-
Analyzing the performance of multilayer neural networks for object recognition
-
5, 6
-
P. Agrawal, R. Girshick, and J. Malik. Analyzing the performance of multilayer neural networks for object recognition. In ECCV. 2014. 5, 6
-
(2014)
ECCV
-
-
Agrawal, P.1
Girshick, R.2
Malik, J.3
-
3
-
-
27844439373
-
A framework for learning predictive structures from multiple tasks and unlabeled data
-
1
-
R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. JMLR, 2005. 1
-
(2005)
JMLR
-
-
Ando, R.K.1
Zhang, T.2
-
6
-
-
70450192888
-
Geometric min-hashing: Finding a (thick) needle in a haystack
-
3
-
O. Chum, M. Perdoch, and J. Matas. Geometric min-hashing: Finding a (thick) needle in a haystack. In CVPR, 2009. 3
-
(2009)
CVPR
-
-
Chum, O.1
Perdoch, M.2
Matas, J.3
-
7
-
-
50649105215
-
Total recall: Automatic query expansion with a generative feature model for object retrieval
-
6
-
O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In ICCV, 2007. 6
-
(2007)
ICCV
-
-
Chum, O.1
Philbin, J.2
Sivic, J.3
Isard, M.4
Zisserman, A.5
-
8
-
-
84898774288
-
Segmentation driven object detection with Fisher vectors
-
6
-
R. G. Cinbis, J. Verbeek, and C. Schmid. Segmentation driven object detection with Fisher vectors. In ICCV, 2013. 6
-
(2013)
ICCV
-
-
Cinbis, R.G.1
Verbeek, J.2
Schmid, C.3
-
9
-
-
56449095373
-
A unified architecture for natural language processing: Deep neural networks with multitask learning
-
1, 2
-
R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML, 2008. 1, 2
-
(2008)
ICML
-
-
Collobert, R.1
Weston, J.2
-
10
-
-
85198028989
-
Imagenet: A large-scale hierarchical image database
-
4
-
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 4
-
(2009)
CVPR
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.-J.4
Li, K.5
Fei-Fei, L.6
-
11
-
-
84898936638
-
Mid-level visual element discovery as discriminative mode seeking
-
3
-
C. Doersch, A. Gupta, and A. A. Efros. Mid-level visual element discovery as discriminative mode seeking. In NIPS, 2013. 3
-
(2013)
NIPS
-
-
Doersch, C.1
Gupta, A.2
Efros, A.A.3
-
12
-
-
84959227156
-
Context as supervisory signal: Discovering objects with predictable context
-
2, 7, 8
-
C. Doersch, A. Gupta, and A. A. Efros. Context as supervisory signal: Discovering objects with predictable context. In ECCV. 2014. 2, 7, 8
-
(2014)
ECCV
-
-
Doersch, C.1
Gupta, A.2
Efros, A.A.3
-
13
-
-
84872258949
-
What makes Paris look like Paris?
-
3, 6, 7
-
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. A. Efros. What makes Paris look like Paris? SIGGRAPH, 2012. 3, 6, 7
-
(2012)
SIGGRAPH
-
-
Doersch, C.1
Singh, S.2
Gupta, A.3
Sivic, J.4
Efros, A.A.5
-
15
-
-
77951298115
-
The pascal visual object classes (voc) challenge
-
5
-
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. IJCV, 2010. 5
-
(2010)
IJCV
-
-
Everingham, M.1
Van Gool, L.2
Williams, C.K.3
Winn, J.4
Zisserman, A.5
-
16
-
-
84887388528
-
Clustering by composition-unsupervised discovery of image categories
-
3
-
A. Faktor and M. Irani. clustering by composition-unsupervised discovery of image categories. In ECCV. 2012. 3
-
(2012)
ECCV
-
-
Faktor, A.1
Irani, M.2
-
18
-
-
0000188120
-
Learning invariance from transformation sequences
-
3
-
P. Földiák. Learning invariance from transformation sequences. Neural Computation, 1991. 3
-
(1991)
Neural Computation
-
-
Földiák, P.1
-
19
-
-
84911400494
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
1, 5, 6
-
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1, 5, 6
-
(2014)
CVPR
-
-
Girshick, R.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
20
-
-
33845575890
-
Unsupervised learning of categories from sets of partially matching image features
-
3, 6
-
K. Grauman and T. Darrell. Unsupervised learning of categories from sets of partially matching image features. In CVPR, 2006. 3, 6
-
(2006)
CVPR
-
-
Grauman, K.1
Darrell, T.2
-
21
-
-
77955989565
-
Image webs: Computing and exploiting connectivity in image collections
-
3
-
K. Heath, N. Gelfand, M. Ovsjanikov, M. Aanjaneya, and L. J. Guibas. Image webs: Computing and exploiting connectivity in image collections. In CVPR, 2010. 3
-
(2010)
CVPR
-
-
Heath, K.1
Gelfand, N.2
Ovsjanikov, M.3
Aanjaneya, M.4
Guibas, L.J.5
-
25
-
-
84913580146
-
Caffe: Convolutional architecture for fast feature embedding
-
4
-
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-MM, 2014. 4
-
(2014)
ACM-MM
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
Guadarrama, S.7
Darrell, T.8
-
26
-
-
84887325186
-
Blocks that shout: Distinctive parts for scene classification
-
3
-
M. Juneja, A. Vedaldi, C. V. Jawahar, and A. Zisserman. Blocks that shout: Distinctive parts for scene classification. In CVPR, 2013. 3
-
(2013)
CVPR
-
-
Juneja, M.1
Vedaldi, A.2
Jawahar, C.V.3
Zisserman, A.4
-
27
-
-
51949105707
-
Unsupervised modeling of object categories using link analysis techniques
-
3
-
G. Kim, C. Faloutsos, and M. Hebert. Unsupervised modeling of object categories using link analysis techniques. In CVPR, 2008. 3
-
(2008)
CVPR
-
-
Kim, G.1
Faloutsos, C.2
Hebert, M.3
-
29
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
1, 3
-
A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 1, 3
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.3
-
30
-
-
84862524901
-
The neural autoregressive distribution estimator
-
2
-
H. Larochelle and I. Murray. The neural autoregressive distribution estimator. In AISTATS, 2011. 2
-
(2011)
AISTATS
-
-
Larochelle, H.1
Murray, I.2
-
31
-
-
84890478042
-
Building high-level features using large scale unsupervised learning
-
2
-
Q. V. Le. Building high-level features using large scale unsupervised learning. In ICASSP, 2013. 2
-
(2013)
ICASSP
-
-
Le, Q.V.1
-
33
-
-
68849114784
-
Foreground focus: Unsupervised learning from partially matching images
-
3
-
Y. J. Lee and K. Grauman. Foreground focus: Unsupervised learning from partially matching images. IJCV, 2009. 3
-
(2009)
IJCV
-
-
Lee, Y.J.1
Grauman, K.2
-
34
-
-
84887327253
-
Harvesting mid-level visual concepts from large-scale internet images
-
3
-
Q. Li, J. Wu, and Z. Tu. Harvesting mid-level visual concepts from large-scale internet images. In CVPR, 2013. 3
-
(2013)
CVPR
-
-
Li, Q.1
Wu, J.2
Tu, Z.3
-
36
-
-
84858736606
-
Beyond categories: The visual memex model for reasoning about object relationships
-
2
-
T. Malisiewicz and A. Efros. Beyond categories: The visual memex model for reasoning about object relationships. In NIPS, 2009. 2
-
(2009)
NIPS
-
-
Malisiewicz, T.1
Efros, A.2
-
37
-
-
84898956512
-
Distributed representations of words and phrases and their compositionality
-
1, 2
-
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013. 1, 2
-
(2013)
NIPS
-
-
Mikolov, T.1
Sutskever, I.2
Chen, K.3
Corrado, G.S.4
Dean, J.5
-
38
-
-
80053344834
-
A discriminative language model with pseudo-negative samples
-
1, 2
-
D. Okanohara and J. Tsujii. A discriminative language model with pseudo-negative samples. In ACL, 2007. 1, 2
-
(2007)
ACL
-
-
Okanohara, D.1
Tsujii, J.2
-
39
-
-
0029938380
-
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
-
2
-
B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996. 2
-
(1996)
Nature
-
-
Olshausen, B.A.1
Field, D.J.2
-
40
-
-
84860632234
-
From a set of shapes to object discovery
-
3
-
N. Payet and S. Todorovic. From a set of shapes to object discovery. In ECCV. 2010. 3
-
(2010)
ECCV
-
-
Payet, N.1
Todorovic, S.2
-
41
-
-
70349152349
-
World-scale mining of objects and events from community photo collections
-
3, 6
-
T. Quack, B. Leibe, and L. Van Gool. World-scale mining of objects and events from community photo collections. In CIVR, 2008. 3, 6
-
(2008)
CIVR
-
-
Quack, T.1
Leibe, B.2
Van Gool, L.3
-
42
-
-
84959250728
-
Dataset fingerprints: Exploring image collections through data mining
-
3, 6
-
K. Rematas, B. Fernando, F. Dellaert, and T. Tuytelaars. Dataset fingerprints: Exploring image collections through data mining. In CVPR, 2015. 3, 6
-
(2015)
CVPR
-
-
Rematas, K.1
Fernando, B.2
Dellaert, F.3
Tuytelaars, T.4
-
43
-
-
84919796093
-
Stochastic backpropagation and approximate inference in deep generative models
-
2
-
D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. ICML, 2014. 2
-
(2014)
ICML
-
-
Rezende, D.J.1
Mohamed, S.2
Wierstra, D.3
-
44
-
-
33845596932
-
Using multiple segmentations to discover objects and their extent in image collections
-
2, 6
-
B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. In CVPR, 2006. 2, 6
-
(2006)
CVPR
-
-
Russell, B.C.1
Freeman, W.T.2
Efros, A.A.3
Sivic, J.4
Zisserman, A.5
-
46
-
-
84933585162
-
Very deep convolutional networks for large-scale image recognition
-
6
-
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, 2014. 6
-
(2014)
CoRR
-
-
Simonyan, K.1
Zisserman, A.2
-
47
-
-
84884958786
-
Unsupervised discovery of mid-level discriminative patches
-
3, 6, 7
-
S. Singh, A. Gupta, and A. A. Efros. Unsupervised discovery of mid-level discriminative patches. In ECCV, 2012. 3, 6, 7
-
(2012)
ECCV
-
-
Singh, S.1
Gupta, A.2
Efros, A.A.3
-
48
-
-
33745938597
-
Discovering objects and their location in images
-
2, 6
-
J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, andW. T. Freeman. Discovering objects and their location in images. In ICCV, 2005. 2, 6
-
(2005)
ICCV
-
-
Sivic, J.1
Russell, B.C.2
Efros, A.A.3
Zisserman, A.4
Freeman, A.T.5
-
49
-
-
84898806407
-
Learning discriminative part detectors for image classification and cosegmentation
-
3
-
J. Sun and J. Ponce. Learning discriminative part detectors for image classification and cosegmentation. In ICCV, 2013. 3
-
(2013)
ICCV
-
-
Sun, J.1
Ponce, J.2
-
50
-
-
84965111399
-
Generative image modeling using spatial lstms
-
2
-
L. Theis and M. Bethge. Generative image modeling using spatial lstms. In NIPS, 2015. 2
-
(2015)
NIPS
-
-
Theis, L.1
Bethge, M.2
-
51
-
-
84949572890
-
-
6 arXiv preprint arXiv:1503. 01817
-
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li. The new data and new challenges in multimedia research. ArXiv preprint arXiv:1503. 01817, 2015. 6
-
(2015)
The New Data and New Challenges in Multimedia Research
-
-
Thomee, B.1
Shamma, D.A.2
Friedland, G.3
Elizalde, B.4
Ni, K.5
Poland, D.6
Borth, D.7
Li, L.-J.8
-
52
-
-
80052908300
-
Unbiased look at dataset bias
-
6, 7
-
A. Torralba and A. A. Efros. Unbiased look at dataset bias. In CVPR, 2011. 6, 7
-
(2011)
CVPR
-
-
Torralba, A.1
Efros, A.A.2
-
53
-
-
56449089103
-
Extracting and composing robust features with denoising autoencoders
-
2
-
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008. 2
-
(2008)
ICML
-
-
Vincent, P.1
Larochelle, H.2
Bengio, Y.3
Manzagol, P.-A.4
-
54
-
-
84973889989
-
Unsupervised learning of visual representations using videos
-
3
-
X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. In ICCV, 2015. 3
-
(2015)
ICCV
-
-
Wang, X.1
Gupta, A.2
-
55
-
-
84898769710
-
Regionlets for generic object detection
-
6
-
X. Wang, M. Yang, S. Zhu, and Y. Lin. Regionlets for generic object detection. In ICCV, 2013. 6
-
(2013)
ICCV
-
-
Wang, X.1
Yang, M.2
Zhu, S.3
Lin, Y.4
-
56
-
-
0036546660
-
Slow feature analysis:unsupervised learning of invariances
-
3
-
L. Wiskott and T. J. Sejnowski. Slow feature analysis:unsupervised learning of invariances. Neural Computation, 2002. 3
-
(2002)
Neural Computation
-
-
Wiskott, L.1
Sejnowski, T.J.2
|