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Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in "any scene" and "any downstream task".
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision , vol. 60, no. 2, pp. 91–110, 2004
2004
Earlier work this paper cites.
K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE transactions on pattern analysis and machine intelligence , vol. 27, no. 10, pp. 1615–1630, 2005
2005
Earlier work this paper cites.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in 2011 International conference on computer vision . Ieee, 2011, pp. 2564–2571
2011
Earlier work this paper cites.
T. Lindeberg, “Scale invariant feature transform,” 2012
2012
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2016, pp. 4104–4113
2016
Earlier work this paper cites.
K. M. Yi, E. Trulls, V. Lepetit, and P. Fua, “Lift: Learned invariant feature transform,” in European Conference on Computer Vision . Springer, 2016, pp. 467–483
2016
Earlier work this paper cites.
V. Balntas, K. Lenc, A. Vedaldi, and K. Mikolajczyk, “Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 5173–5182
2017
Earlier work this paper cites.
R. Mur-Artal and J. D. Tardós, “Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE Transactions on Robotics , vol. 33, no. 5, pp. 1255–1262, 2017
2017
Earlier work this paper cites.
Y. Tian, B. Fan, and F. Wu, “L2-net: Deep learning of discriminative patch descriptor in euclidean space,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 661–669
2017
Earlier work this paper cites.
A. Mishchuk, D. Mishkin, F. Radenovic, and J. Matas, “Working hard to know your neighbor’s margins: Local descriptor learning loss,” in Advances in Neural Information Processing Systems , 2017, pp. 4826–4837
2017
Earlier work this paper cites.
C. Toft, C. Olsson, and F. Kahl, “Long-term 3d localization and pose from semantic labellings,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 650–659
2017
Earlier work this paper cites.
A. Mishchuk, D. Mishkin, F. Radenovic, and J. Matas, “Working hard to know your neighbor’s margins: Local descriptor learning loss,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han, “Large-scale image retrieval with attentive deep local features,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 3456–3465
2017
Earlier work this paper cites.
J. L. Schonberger, H. Hardmeier, T. Sattler, and M. Pollefeys, “Comparative evaluation of hand-crafted and learned local features,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1482–1491
2017
Earlier work this paper cites.
T. Sattler, W. Maddern, C. Toft, A. Torii, L. Hammarstrand, E. Stenborg, D. Safari, M. Okutomi, M. Pollefeys, J. Sivic et al. , “Benchmarking 6dof outdoor visual localization in changing conditions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 8601–8610
2018
Earlier work this paper cites.
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2018, pp. 224–236
2018
Earlier work this paper cites.
J. L. Schönberger, M. Pollefeys, A. Geiger, and T. Sattler, “Semantic visual localization,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6896–6906
2018
Earlier work this paper cites.
D. Mishkin, F. Radenovic, and J. Matas, “Repeatability is not enough: Learning affine regions via discriminability,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 284–300
2018
Earlier work this paper cites.
Y. Ono, E. Trulls, P. Fua, and K. M. Yi, “Lf-net: learning local features from images,” in Advances in neural information processing systems , 2018, pp. 6234–6244
2018
Earlier work this paper cites.
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 224–236
2018
Earlier work this paper cites.
C. Toft, E. Stenborg, L. Hammarstrand, L. Brynte, M. Pollefeys, T. Sattler, and F. Kahl, “Semantic match consistency for long-term visual localization,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 383–399
2018
Earlier work this paper cites.
Y. Ono, E. Trulls, P. Fua, and K. M. Yi, “Lf-net: Learning local features from images,” Advances in neural information processing systems , vol. 31, 2018
2018
Cited alongside, same era.
T. Sattler, W. Maddern, C. Toft, A. Torii, L. Hammarstrand, E. Stenborg, D. Safari, M. Okutomi, M. Pollefeys, J. Sivic et al. , “Benchmarking 6dof outdoor visual localization in changing conditions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8601–8610
2018
Cited alongside, same era.
X. Wang, Y. Hua, E. Kodirov, G. Hu, R. Garnier, and N. M. Robertson, “Ranked list loss for deep metric learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5207–5216
2019
Cited alongside, same era.
A. Barroso-Laguna, E. Riba, D. Ponsa, and K. Mikolajczyk, “Key. net: Keypoint detection by handcrafted and learned cnn filters,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 5836–5844
2019
M. Kristan, J. Matas, A. Leonardis, M. Felsberg, R. Pflugfelder, J.-K. Kämäräinen, H. J. Chang, M. Danelljan, L. Cehovin, A. Lukežič et al. , “The ninth visual object tracking vot2021 challenge results,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 2711–2738
2021
Later among the works it cites.
Z. Huang, H. Zhou, Y. Li, B. Yang, Y. Xu, X. Zhou, H. Bao, G. Zhang, and H. Li, “Vs-net: Voting with segmentation for visual localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 6101–6111
2021
Later among the works it cites.
S. Suwanwimolkul, S. Komorita, and K. Tasaka, “Learning of low-level feature keypoints for accurate and robust detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 2262–2271
2021
Later among the works it cites.
F. Xue, I. Budvytis, D. O. Reino, and R. Cipolla, “Efficient large-scale localization by global instance recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 348–17 357
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Y. Tian, X. Yu, B. Fan, F. Wu, H. Heijnen, and V. Balntas, “Sosnet: Second order similarity regularization for local descriptor learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 016–11 025
2019
Cited alongside, same era.
Z. Luo, T. Shen, L. Zhou, J. Zhang, Y. Yao, S. Li, T. Fang, and L. Quan, “Contextdesc: Local descriptor augmentation with cross-modality context,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2527–2536
2019
Cited alongside, same era.
J. Revaud, C. De Souza, M. Humenberger, and P. Weinzaepfel, “R2d2: Reliable and repeatable detector and descriptor,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/3198dfd0aef271d22f7bcddd6f12f5cb-Paper.pdf
2019
Cited alongside, same era.
M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler, “D2-net: A trainable cnn for joint detection and description of local features,” in CVPR 2019 , 2019
2019
Cited alongside, same era.
W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3967–3976
2019
Cited alongside, same era.
M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler, “D2-net: A trainable cnn for joint description and detection of local features,” in Proceedings of the ieee/cvf conference on computer vision and pattern recognition , 2019, pp. 8092–8101
2019
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2019
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M. Tyszkiewicz, P. Fua, and E. Trulls, “Disk: Learning local features with policy gradient,” Advances in Neural Information Processing Systems , vol. 33, pp. 14 254–14 265, 2020
2020
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2022
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C. Wang, R. Xu, Y. Zhang, S. Xu, W. Meng, B. Fan, and X. Zhang, “Mtldesc: Looking wider to describe better,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 2388–2396
2022
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B. Fan, J. Zhou, W. Feng, H. Pu, Y. Yang, Q. Kong, F. Wu, and H. Liu, “Learning semantic-aware local features for long term visual localization,” IEEE Transactions on Image Processing , vol. 31, pp. 4842–4855, 2022
2022
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A. Barroso-Laguna and K. Mikolajczyk, “Key. net: Keypoint detection by handcrafted and learned cnn filters revisited,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 1, pp. 698–711, 2022
2022
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X. Zhao, X. Wu, J. Miao, W. Chen, P. C. Chen, and Z. Li, “Alike: Accurate and lightweight keypoint detection and descriptor extraction,” IEEE Transactions on Multimedia , 2022
2022
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K. Li, L. Wang, L. Liu, Q. Ran, K. Xu, and Y. Guo, “Decoupling makes weakly supervised local feature better,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 15 838–15 848
2022
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