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Convolutional networks trained on large supervised dataset produce visual features which form the basis for the state-of-the-art in many computer-vision problems.
Zipf’s law and the internet
L.A. Adamic and B.A. Huberman · 2002
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Dataset issues in object recognition
J. Ponce, T.L. Berg, M. Everingham, D.A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B.C. Russell, A. Torralba, C.K.I. Williams, J. Zhang, and A. Zisserman · 2006
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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Visualizing data using t-SNE
L.J.P. van der Maaten and G.E. Hinton · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei · 2009
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Observing human-object interactions: Using spatial and functional compatibility for recognition
A. Gupta, A. Kembhavi, and L.S. Davis · 2009
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Recognizing indoor scenes
A. Quattoni and A.Torralba · 2009
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Ranking with ordered weighted pairwise classification
N. Usunier, D. Buffoni, and P. Gallinari · 2009
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Harvesting large-scale weakly tagged image databases from the web
J. Fan, Y. Shen, N. Zhou, and Y. Gao · 2010
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Every picture tells a story: Generating sentences from images
Ali Farhadi, Mohsen Hejrati, Mohammad Amin Sadeghi, Peter Young, Cyrus Rashtchian, Julia Hockenmaier, and David Forsyth · 2010
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Optimol: automatic online picture collection via incremental model learning
L.-J. Li and L. Fei-Fei · 2010
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Efficient object category recognition using classemes
L. Torresani, M. Szummer, and A. Fitzgibbon · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba · 2010
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The devil is in the details: an evaluation of recent feature encoding methods
K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman · 2011
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Learning cross-modality similarity for multinomial data
Y. Jia, M. Salzmann, and T. Darrell · 2011
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Im2text: Describing images using 1 million captioned photographs
V. Ordonez, G. Kulkarni, and T.L. Berg · 2011
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Unbiased look at dataset bias
A. Torralba and A.A. Efros · 2011
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Wsabie: Scaling up to large vocabulary image annotation
J. Weston, S. Bengio, and N. Usunier · 2011
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Corpus-guided sentence generation of natural images
Y. Yang, C. Teo, H. Daumé III, and Y. Aloimonos · 2011
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Human action recognition by learning bases of action attributes and parts
B. Yao, X. Jiang, A. Khosla, A.L. Lin, L.J. Guibas, and L. Fei-Fei · 2011
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Distributional semantics in technicolor
E. Bruni, G. Boleda, M. Baroni, and N.K. Tran · 2012
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How does the brain solve visual object recognition?
J.J. DiCarlo, D. Zoccolan, and N.C. Rust NC · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G.E. Hinton · 2012
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Multimodal learning with deep boltzmann machines
N. Srivastava and R. Salakhutdinov · 2012
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Devise: A deep visual-semantic embedding model
A. Frome, G. Corrado, J. Shlens, S. Bengio, J. Dean, and T. Mikolov · 2013
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Framing image description as a ranking task: Data, models and evaluation metrics
M. Hodosh, P. Young, and J. Hockenmaier · 2013
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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CNN features off-the-shelf: an astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Accelerating t-SNE using tree-based algorithms
L.J.P. van der Maaten · 2014
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Well begun is half done: Generating high-quality seeds for automatic image dataset construction from web
Y. Xia, X. Cao, F. Wen, and J. Sun · 2014
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Conceptlearner: Discovering visual concepts from weakly labeled image collections
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Some improvements on deep convolutional neural network based image classification
A.G. Howard · 2013
Cited alongside, same era.
Harvesting mid-level visual concepts from large-scale internet images
Q. Li, J. Wu, and Z. Tu · 2013
Cited alongside, same era.
Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
Cited alongside, same era.
Unsupervised joint object discovery and segmentation in internet images
M. Rubinstein, A. Joulin, J. Kopf, and C. Liu · 2013
Cited alongside, same era.
Zero-shot learning through cross-modal transfer
R. Socher, M. Ganjoo, C.D. Manning, and A. Ng · 2013
Cited alongside, same era.
Good practice in large-scale learning for image classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2014
Cited alongside, same era.
B. Zhou, V. Jagadeesh, and R. Piramuthu · 2014
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Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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VQA: Visual question answering, 2015
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C.L. Zitnick, and D. Parikh · 2015
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Webly supervised learning of convolutional networks
X. Chen and A. Gupta · 2015
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User conditional hashtag prediction for images
E. Denton, J. Weston, M. Paluri, L. Bourdev, and R. Fergus · 2015
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The pascal visual object classes challenge — a retrospective
M. Everingham, S.M.A. Eslami, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman · 2015
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Deep classifiers from image tags in the wild
H. Izadinia, B.C. Russell, A. Farhadi, M.D. Hoffman, and A. Hertzmann · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Large-scale deep learning on the yfcc100m dataset
K. Ni, R. Pearce, E. Wang, K. Boakye, B. Van Essen, D. Borth, and B. Chen · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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The new data and new challenges in multimedia research
B. Thomee, D.A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li · 2015
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Visual madlibs: Fill in the blank description generation and question answering
L. Yu, E. Park, A.C. Berg, and T.L. Berg · 2015
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Learning to segment object candidates
P. Pinheiro, R. Collobert, and P. Dollár · 2016
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