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Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition.
Handwritten digit recognition with a back-propagation network
LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L.: · 1990
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Video Google: A text retrieval approach to object matching in videos
Sivic, J., Zisserman, A.: · 2003
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Visual categorization with bags of keypoints
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: · 2004
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Distinctive image features from scale-invariant keypoints
Lowe, D.G.: · 2004
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The pyramid match kernel: Discriminative classification with sets of image features
Grauman, K., Darrell, T.: · 2005
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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
Lazebnik, S., Schmid, C., Ponce, J.: · 2006
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Fisher kernels on visual vocabularies for image categorization
Perronnin, F., Dance, C.R.: · 2007
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Hamming embedding and weak geometric consistency for large-scale image search
Jégou, H., Douze, M., Schmid, C.: · 2008
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: · 2009
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Recognizing indoor scenes
Quattoni, A., Torralba, A.: · 2009
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Aggregating local descriptors into a compact image representation
Jégou, H., Douze, M., Schmid, C., Pérez, P.: · 2010
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Locality-constrained linear coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: · 2010
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Improving the Fisher kernel for large-scale image classification
Perronnin, F., Sanchez, J., Mensink, T.: · 2010
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Large-scale image retrieval with compressed Fisher vectors
Perronnin, F., Liu, Y., Sánchez, J., Poirier, H.: · 2010
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SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: · 2010
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Scene recognition and weakly supervised object localization with deformable part-based models
Pandey, M., Lazebnik, S.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.: · 2012
Cited alongside, same era.
Building high-level features using large scale unsupervised learning
Le, Q., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., Ng, A.: · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2013
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Visualizing and understanding convolutional neural networks
Zeiler, M.D., Fergus, R.: · 2013
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Caffe: An open source convolutional architecture for fast feature embedding
Jia, Y.: · 2013
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Leveraging structure from motion to learn discriminative codebooks for scalable landmark classification
Bergamo, A., Sinha, S.N., Torresani, L.: · 2013
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Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: · 2012
Cited alongside, same era.
Large scale visual recognition challenge
Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., Fei-Fei, L.: · 2012
Cited alongside, same era.
Unsupervised discovery of mid-level discriminative patches
Singh, S., Gupta, A., Efros, A.A.: · 2012
Cited alongside, same era.
Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening
Jégou, H., Chum, O.: · 2012
Cited alongside, same era.
Leveraging category-level labels for instance-level image retrieval
Gordo, A., Rodrıguez-Serrano, J.A., Perronnin, F., Valveny, E.: · 2012
Cited alongside, same era.
Maxout networks
Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: · 2013
Cited alongside, same era.
Regularization of neural networks using DropConnect
Wan, L., Zeiler, M., Zhang, S., Lecun, Y., Fergus, R.: · 2013
Cited alongside, same era.
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Detecting avocados to zucchinis: what have we done, and where are we going?
Russakovsky, O., Deng, J., Huang, Z., Berg, A., Fei-Fei, L.: · 2013
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Good practice in large-scale learning for image classification
Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C., et al.: · 2013
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Image Classification with the Fisher Vector: Theory and Practice
Sanchez, J., Perronnin, F., Mensink, T., Verbeek, J.: · 2013
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Blocks that shout: Distinctive parts for scene classification
Juneja, M., Vedaldi, A., Jawahar, C.V., Zisserman, A.: · 2013
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Mid-level visual element discovery as discriminative mode seeking
Doersch, C., Gupta, A., Efros, A.A.: · 2013
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To aggregate or not to aggregate: selective match kernels for image search
Tolias, G., Avrithis, Y., Jégou, H.: · 2013
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Learning and transferring mid-level image representations using convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., Sivic, J., et al.: · 2014
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CNN features off-the-shelf: An astounding baseline for recognition
Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: · 2014
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DenseNet: Implementing efficient convnet descriptor pyramids
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., Keutzer, K.: · 2014
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