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Several recent approaches showed how the representations learned by Convolutional Neural Networks can be repurposed for novel tasks.
Scalable recognition with a vocabulary tree
D. Nister and H. Stewénius · 2006
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Object retrieval with large vocabularies and fast spatial matching
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman · 2007
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IM2GPS: estimating geographic information from a single image
J. Hays and A. A. Efros · 2008
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Learning a fine vocabulary
A. Mikulik, M. Perdoch, O. Chum, and J. Matas · 2010
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SUN Database: Large-scale Scene Recognition from Abbey to Zoo
J. Xiao, K. E. J. Hays, A. Oliva, and A. Torralba · 2010
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Leveraging category-level labels for instance-level image retrieval
A. Gordo, J. A. Rodriguez-Serrano, F. Perronnin, and E. Valveny · 2012
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Imagenet classification with deep convolutional networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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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 · 2012
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Good Practice in Large-Scale Learning for Image Classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition, 2013
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
Cited alongside, same era.
To aggregate or not to aggregate: selective match kernels for image search
G. Tolias, Y. Avrithis, and H. Jegou · 2013
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Factors of transferability for a generic convnet representation
H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2014
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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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Deep fisher networks for large-scale image classification
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. S.Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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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 · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Object detectors emerge in deep scene CNNs
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Cited alongside, same era.
Multi-scale orderless pooling of deep convolutional activation features, 2014
Y. Gong, L. Wang, R. Guo, and S. Lazebnik · 2014
Cited alongside, same era.
Negative evidences and co-occurrences in image retrieval: the benefit of pca and whitening
H. Jegou and O. Chum · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2014
Cited alongside, same era.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 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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A baseline for visual instance retrieval with deep convolutional networks
A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2015
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