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We propose an efficient method to learn deep local descriptors for instance-level recognition.
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)
2003
Earlier work this paper cites.
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing
2004
Earlier work this paper cites.
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI
2005
Earlier work this paper cites.
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. IJCV
2005
Earlier work this paper cites.
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)
2007
Earlier work this paper cites.
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. Computer Vision and Image Understanding
2008
Earlier work this paper cites.
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: CVPR (Jun 2008)
2008
Earlier work this paper cites.
Jégou, H., Douze, M., Schmid, C.: On the burstiness of visual elements. In: CVPR (Jun 2009)
2009
Earlier work this paper cites.
Perronnin, F., Liu, Y., Renders, J.M.: A family of contextual measures of similarity between distributions with application to image retrieval. In: CVPR. pp. 2358–2365 (2009)
2009
Earlier work this paper cites.
Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. IJCV
2010
Earlier work this paper cites.
Perronnin, F., Liu, Y., Sanchez, J., Poirier, H.: Large-scale image retrieval with compressed Fisher vectors. In: CVPR (2010)
2010
Earlier work this paper cites.
Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. PAMI
2011
Earlier work this paper cites.
Jégou, H., Chum, O.: Negative evidences and co-occurences in image retrieval: The benefit of PCA and whitening. In: ECCV (Oct 2012)
2012
Earlier work this paper cites.
Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating local descriptors into compact codes. In: PAMI (Sep 2012)
2012
Earlier work this paper cites.
Arandjelović, R., Zisserman, A.: All about VLAD. In: CVPR (2013)
2013
Earlier work this paper cites.
Zhu, C.Z., Jégou, H., ichi Satoh, S.: Query-adaptive asymmetrical dissimilarities for visual object retrieval. In: ICCV (2013)
2013
Earlier work this paper cites.
Arandjelović, R., Zisserman, A.: DisLocation: Scalable descriptor distinctiveness for location recognition. In: ACCV (2014)
2014
Earlier work this paper cites.
Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval. In: ECCV (2014)
2014
Cited alongside, same era.
Babenko, A., Lempitsky, V.: Aggregating deep convolutional features for image retrieval. In: ICCV (2015)
2015
Cited alongside, same era.
Iscen, A., Tolias, G., Gosselin, P.H., Jégou, H.: A comparison of dense region detectors for image search and fine-grained classification. IEEE Transactions on Image Processing
2015
Cited alongside, same era.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. IJCV (2015)
2015
Cited alongside, same era.
Schönberger, J.L., Radenović, F., Chum, O., Frahm, J.M.: From single image query to detailed 3D reconstruction. In: CVPR (2015)
2015
Vo, N., Jacobs, N., Hays, J.: Revisiting im2gps in the deep learning era. In: CVPR (2017)
2017
Later among the works it cites.
DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: Self-supervised interest point detection and description. In: CVPRW (2018)
2018
Later among the works it cites.
Radenović, F., Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Revisiting oxford and paris: Large-scale image retrieval benchmarking. In: CVPR (2018)
2018
Later among the works it cites.
Barroso Laguna, A., Riba, E., Ponsa, D., Mikolajczyk, K.: Key. net: Keypoint detection by handcrafted and learned cnn filters. In: ICCV (2019)
2019
Later among the works it cites.
Benbihi, A., Geist, M., Pradalier, C.: Elf: Embedded localisation of features in pre-trained cnn. In: CVPR (2019)
2019
Later among the works it cites.
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Cited alongside, same era.
Tolias, G., Avrithis, Y., Jégou, H.: Image search with selective match kernels: aggregation across single and multiple images. IJCV (2015)
2015
Cited alongside, same era.
Yue-Hei Ng, J., Yang, F., Davis, L.S.: Exploiting local features from deep networks for image retrieval. In: CVPR (2015)
2015
Cited alongside, same era.
Arandjelović, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: CVPR (2016)
2016
Cited alongside, same era.
Husain, S., Bober, M.: Improving large-scale image retrieval through robust aggregation of local descriptors. PAMI
2016
Cited alongside, same era.
Kalantidis, Y., Mellina, C., Osindero, S.: Cross-dimensional weighting for aggregated deep convolutional features. In: ECCVW (2016)
2016
Cited alongside, same era.
Mohedano, E., McGuinness, K., O’Connor, N.E., Salvador, A., Marques, F., Giro-i Nieto, X.: Bags of local convolutional features for scalable instance search. In: ICMR (2016)
2016
Cited alongside, same era.
Razavian, A.S., Sullivan, J., Carlsson, S., Maki, A.: Visual instance retrieval with deep convolutional networks. ITE Trans. on Media Technology and Applications (2016)
2016
Cited alongside, same era.
Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., Sattler, T.: D2-net: A trainable cnn for joint detection and description of local features. In: CVPR (2019)
2019
Later among the works it cites.
Gu, Y., Li, C., Jiang, Y.G.: Towards optimal cnn descriptors for large-scale image retrieval. In: ACM Multimedia (2019)
2019
Later among the works it cites.
Radenović, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. PAMI
2019
Later among the works it cites.
Revaud, J., Almazán, J., de Rezende, R.S., de Souza, C.R.: Learning with average precision: Training image retrieval with a listwise loss. In: ICCV (2019)
2019
Later among the works it cites.
Revaud, J., Weinzaepfel, P., De Souza, C., Pion, N., Csurka, G., Cabon, Y., Humenberger, M.: R2d2: Repeatable and reliable detector and descriptor. In: NeurIPS (2019)
2019
Later among the works it cites.
Siméoni, O., Avrithis, Y., Chum, O.: Local features and visual words emerge in activations. In: CVPR (2019)
2019
Later among the works it cites.
Teichmann, M., Araujo, A., Zhu, M., Sim, J.: Detect-to-retrieve: Efficient regional aggregation for image search. In: CVPR (2019)
2019
Later among the works it cites.
Bhowmik, A., Gumhold, S., Rother, C., Brachmann, E.: Reinforced feature points: Optimizing feature detection and description for a high-level task. In: CVPR (2020)
2020
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Cao, B., Araujo, A., Sim, J.: Unifying deep local and global features for efficient image search. In: arxiv (2020)
2020
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Wang, Q., Zhou, X., Hariharan, B., Snavely, N.: Learning feature descriptors using camera pose supervision. In: arXiv (2020)
2020
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Weyand, T., Araujo, A., Cao, B., Sim, J.: Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval. In: CVPR (2020)
2020
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Yang, T., Nguyen, D., Heijnen, H., Balntas, V.: Ur2kid: Unifying retrieval, keypoint detection, and keypoint description without local correspondence supervision. In: arxiv (2020)
2020
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