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Visual Place Recognition (VPR) is a critical task for performing global re-localization in visual perception systems.
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R. Arandjelović, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, “NetVLAD: CNN architecture for weakly supervised place recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
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F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and ¡0.5mb model size,” 2016
2016
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S. Markidis, S. W. D. Chien, E. Laure, I. B. Peng, and J. S. Vetter, “Nvidia tensor core programmability, performance & precision,” in 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) , 2018, pp. 522–531
2018
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D. Olid, J. M. Fácil, and J. Civera, “Single-view place recognition under seasonal changes,” in PPNIV Workshop at IROS 2018 , 2018
2018
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M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” 2018
2018
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F. Radenović, G. Tolias, and O. Chum, “Fine-tuning cnn image retrieval with no human annotation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 7, pp. 1655–1668, 2019
2019
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J. Bethge, H. Yang, M. Bornstein, and C. Meinel, “Binarydensenet: Developing an architecture for binary neural networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops , Oct 2019
2019
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S. Hausler, S. Garg, M. Xu, M. Milford, and T. Fischer, “Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 141–14 152
2021
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2021
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S. Yun and A. Wong, “Do all mobilenets quantize poorly? gaining insights into the effect of quantization on depthwise separable convolutional networks through the eyes of multi-scale distributional dynamics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2021, pp. 2447–2456
2021
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A. Ali-bey, B. Chaib-draa, and P. Giguère, “Gsv-cities: Toward appropriate supervised visual place recognition,” Neurocomputing , vol. 513, pp. 194–203, 2022
2022
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G. Berton, C. Masone, and B. Caputo, “Rethinking visual geo-localization for large-scale applications,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 4878–4888
2022
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G. Berton, R. Mereu, G. Trivigno, C. Masone, G. Csurka, T. Sattler, and B. Caputo, “Deep visual geo-localization benchmark,” in CVPR , June 2022
2022
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B. Ferrarini, M. Milford, K. D. McDonald-Maier, and S. Ehsan, “Highly-efficient binary neural networks for visual place recognition,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 5493–5500
2022
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B. Ferrarini, M. J. Milford, K. D. McDonald-Maier, and S. Ehsan, “Binary neural networks for memory-efficient and effective visual place recognition in changing environments,” IEEE Transactions on Robotics , vol. 38, no. 4, pp. 2617–2631, 2022
2022
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S. Mokssit, D. B. Licea, B. Guermah, and M. Ghogho, “Deep learning techniques for visual slam: A survey,” IEEE Access , vol. 11, pp. 20 026–20 050, 2023
2023
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