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Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks.
Video Google: A text retrieval approach to object matching in videos
Sivic, J., Zisserman, A.: · 2003
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Distinctive image features from scale-invariant keypoints
Lowe, D.: · 2004
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., LeCun, Y.: · 2005
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A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: · 2005
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., LeCun, Y.: · 2006
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Object retrieval with large vocabularies and fast spatial matching
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: · 2007
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Improving descriptors for fast tree matching by optimal linear projection
Mikolajczyk, K., Matas, J.: · 2007
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Lost in quantization: Improving particular object retrieval in large scale image databases
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: · 2008
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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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Large-scale image retrieval with compressed Fisher vectors
Perronnin, F., Liu, Y., Sanchez, J., Poirier, H.: · 2010
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Large-scale discovery of spatially related images
Chum, O., Matas, J.: · 2010
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Building Rome on a cloudless day
Frahm, J.M., Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: · 2010
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Location recognition using prioritized feature matching
Li, Y., Snavely, N., Huttenlocher, D.P.: · 2010
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Total recall II: Query expansion revisited
Chum, O., Mikulik, A., Perdoch, M., Matas, J.: · 2011
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Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors
Danfeng, Q., Gammeter, S., Bossard, L., Quack, T., Gool, L.V.: · 2011
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Geometric latent dirichlet allocation on a matching graph for large-scale image datasets
Philbin, J., Sivic, J., Zisserman, A.: · 2011
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Building Rome in a day
Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S.M., Szeliski, R.: · 2011
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Approximate Gaussian mixtures for large scale vocabularies
Avrithis, Y., Kalantidis, Y.: · 2012
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Aggregating local descriptors into compact codes
Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Negative evidences and co-occurences in image retrieval: The benefit of PCA and whitening
Jégou, H., Chum, O.: · 2012
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Three things everyone should know to improve object retrieval
Arandjelovic, R., Zisserman, A.: · 2012
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All about VLAD
Arandjelovic, R., Zisserman, A.: · 2013
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DeCAF: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: · 2013
Cited alongside, same era.
Discovering details and scene structure with hierarchical iconoid shift
Weyand, T., Leibe, B.: · 2013
Discriminative deep metric learning for face verification in the wild
Hu, J., Lu, J., Tan, Y.P.: · 2014
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Learning fine-grained image similarity with deep ranking
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Fracking deep convolutional image descriptors
Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Moreno-Noguer, F.: · 2014
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Efficient image detail mining
Mikulík, A., Radenović, F., Chum, O., Matas, J.: · 2014
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Cited alongside, same era.
Learning vocabularies over a fine quantization
Mikulik, A., Perdoch, M., Chum, O., Matas, J.: · 2013
Cited alongside, same era.
Image retrieval for online browsing in large image collections
Mikulik, A., Chum, O., Matas, J.: · 2013
Cited alongside, same era.
Spatially-constrained similarity measure for large-scale object retrieval
Shen, X., Lin, Z., Brandt, J., Wu, Y.: · 2014
Cited alongside, same era.
Visual query expansion with or without geometry: refining local descriptors by feature aggregation
Tolias, G., Jégou, H.: · 2014
Cited alongside, same era.
Orientation covariant aggregation of local descriptors with embeddings
Tolias, G., Furon, T., Jégou, H.: · 2014
Cited alongside, same era.
Neural codes for image retrieval
Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: · 2014
Cited alongside, same era.
Multiple measurements and joint dimensionality reduction for large scale image search with short vectors
Radenović, F., Jegou, H., Chum, O.: · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: · 2015
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Aggregating deep convolutional features for image retrieval
Babenko, A., Lempitsky, V.: · 2015
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Cross-dimensional weighting for aggregated deep convolutional features
Kalantidis, Y., Mellina, C., Osindero, S.: · 2015
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From generic to specific deep representations for visual recognition
Azizpour, H., Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S.: · 2015
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FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., Philbin, J.: · 2015
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Deep metric learning using triplet network
Hoffer, E., Ailon, N.: · 2015
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From single image query to detailed 3D reconstruction
Schönberger, J.L., Radenović, F., Chum, O., Frahm, J.M.: · 2015
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Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection
Papandreou, G., Kokkinos, I., Savalle, P.A.: · 2015
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Particular object retrieval with integral max-pooling of CNN activations
Tolias, G., Sicre, R., Jégou, H.: · 2016
Closest in time.
NetVLAD: CNN architecture for weakly supervised place recognition
Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: · 2016
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Deep image retrieval: Learning global representations for image search
Gordo, A., Almazan, J., Revaud, J., Larlus, D.: · 2016
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Good practice in CNN feature transfer
Zheng, L., Zhao, Y., Wang, S., Wang, J., Tian, Q.: · 2016
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From dusk till dawn: Modeling in the dark
Radenović, F., Schönberger, J.L., Ji, D., Frahm, J.M., Chum, O., Matas, J.: · 2016
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