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This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation.
Z. Chen, J. Lin, V. Chandrasekhar, and L.-Y. Duan, “Gated square-root pooling for image instance retrieval,” in 2018 25th IEEE International Conference on Image Processing (ICIP) . IEEE, 2018, pp. 1982–1986
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H. Jegou, M. Douze, and C. Schmid, “Hamming embedding and weak geometric consistency for large scale image search,” in Computer Vision – ECCV 2008 , D. Forsyth, P. Torr, and A. Zisserman, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 304–317
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J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Lost in quantization: Improving particular object retrieval in large scale image databases,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on . IEEE, 2008, pp. 1–8
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S. A. Chatzichristofis and Y. S. Boutalis, “Cedd: Color and edge directivity descriptor: A compact descriptor for image indexing and retrieval,” in ICVS , 2008, pp. 312–322
2008
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J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 248–255
2009
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H. Jégou, M. Douze, and C. Schmid, “Improving bag-of-features for large scale image search,” vol. 87, no. 3, 2010, pp. 316–336. [Online]. Available: https://doi.org/10.1007/s11263-009-0285-2
2010
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C. Wengert, M. Douze, and H. Jégou, “Bag-of-colors for improved image search,” in Proceedings of the 19th ACM International Conference on Multimedia , ser. MM ’11. New York, NY, USA: ACM, 2011, pp. 1437–1440. [Online]. Available: http://doi.acm.org/10.1145/2072298.2072034
2011
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V. R. Chandrasekhar, D. M. Chen, S. S. Tsai, N.-M. Cheung, H. Chen, G. Takacs, Y. Reznik, R. Vedantham, R. Grzeszczuk, J. Bach et al. , “The stanford mobile visual search data set,” in Proceedings of the second annual ACM conference on Multimedia systems , 2011, pp. 117–122
2011
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S. Chatzichristofis, C. Iakovidou, Y. Boutalis, and E. Angelopoulou, “Mean normalized retrieval order (mnro): a new content-based image retrieval performance measure,” Multimedia Tools and Applications , pp. 1–32, 2012. [Online]. Available: http://dx.doi.org/10.1007/s11042-012-1192-z
2012
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S. Patil and S. Talbar, “Content based image retrieval using various distance metrics,” in Data Engineering and Management , R. Kannan and F. Andres, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 154–161
2012
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W.-L. Zhao, H. Jégou, and G. Gravier, “Oriented pooling for dense and non-dense rotation-invariant features,” 2013
2013
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L. Zheng, S. Wang, and Q. Tian, “Coupled binary embedding for large-scale image retrieval,” Image Processing, IEEE Transactions on , vol. 23, no. 8, pp. 3368–3380, 2014
2014
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J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, “Deep learning for content-based image retrieval: A comprehensive study,” in Proceedings of the 22nd ACM international conference on Multimedia , 2014, pp. 157–166
2014
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A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: an astounding baseline for recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2014, pp. 806–813
2014
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Y. Gong, L. Wang, R. Guo, and S. Lazebnik, “Multi-scale orderless pooling of deep convolutional activation features,” in Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VII , 2014, pp. 392–407
2014
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A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, “Neural codes for image retrieval,” in European conference on computer vision . Springer, 2014, pp. 584–599
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Cited alongside, same era.
J. Yue-Hei Ng, F. Yang, and L. S. Davis, “Exploiting local features from deep networks for image retrieval,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2015, pp. 53–61
2015
Cited alongside, same era.
L. Zheng, Y. Yang, and Q. Tian, “Sift meets cnn: A decade survey of instance retrieval,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 5, pp. 1224–1244, 2018
2018
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X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803
2018
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H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei, “Relation networks for object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3588–3597
2018
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2018
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M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronnin, and C. Schmid, “Local convolutional features with unsupervised training for image retrieval,” in 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015 , 2015, pp. 91–99
2015
Cited alongside, same era.
M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronin, and C. Schmid, “Local convolutional features with unsupervised training for image retrieval,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 91–99
2015
Cited alongside, same era.
2016
Cited alongside, same era.
T. Salimans and D. P. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” in Advances in neural information processing systems , 2016, pp. 901–909
2016
Cited alongside, same era.
A. Gordo, J. Almazán, J. Revaud, and D. Larlus, “Deep image retrieval: Learning global representations for image search,” in European conference on computer vision . Springer, 2016, pp. 241–257
2016
Cited alongside, same era.
A. S. Razavian, J. Sullivan, S. Carlsson, and A. Maki, “Visual instance retrieval with deep convolutional networks,” ITE Transactions on Media Technology and Applications , vol. 4, no. 3, pp. 251–258, 2016
2016
Cited alongside, same era.
G. Tolias, Y. S. Avrithis, and H. Jégou, “Image search with selective match kernels: Aggregation across single and multiple images,” International Journal of Computer Vision , vol. 116, no. 3, pp. 247–261, 2016
2016
Cited alongside, same era.
Y. Kalantidis, C. Mellina, and S. Osindero, “Cross-dimensional weighting for aggregated deep convolutional features,” in European conference on computer vision . Springer, 2016, pp. 685–701
2016
Cited alongside, same era.
S. Wu, G. Li, L. Deng, L. Liu, D. Wu, Y. Xie, and L. Shi, “ l 1 l1 -norm batch normalization for efficient training of deep neural networks,” IEEE transactions on neural networks and learning systems , vol. 30, no. 7, pp. 2043–2051, 2018
2018
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S. A. Vassou, N. Anagnostopoulos, K. Christodoulou, A. Amanatiadis, and S. A. Chatzichristofis, “Como: a scale and rotation invariant compact composite moment-based descriptor for image retrieval,” Multimedia Tools and Applications , pp. 1–24, 2018
2018
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Q. Wang, B. Li, T. Xiao, J. Zhu, C. Li, D. Wong, and L. Chao, “Learning deep transformer models for machine translation,” 01 2019, pp. 1810–1822
2019
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Z. Zhang, Y. Xie, W. Zhang, and Q. Tian, “Effective image retrieval via multilinear multi-index fusion,” IEEE Transactions on Multimedia , 2019
2019
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C. Iakovidou, N. Anagnostopoulos, M. Lux, K. Christodoulou, Y. S. Boutalis, and S. A. Chatzichristofis, “Composite description based on salient contours and color information for CBIR tasks,” IEEE Trans. Image Processing , vol. 28, no. 6, pp. 3115–3129, 2019. [Online]. Available: https://doi.org/10.1109/TIP.2019.2894281
2019
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2020
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2020
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M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever, “Generative pretraining from pixels,” in International Conference on Machine Learning . PMLR, 2020, pp. 1691–1703
2020
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2020
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T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020
2020
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B. Cao, A. Araujo, and J. Sim, “Unifying deep local and global features for image search,” in European Conference on Computer Vision . Springer, 2020, pp. 726–743
2020
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