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This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled.
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V. A. Sindagi, R. Yasarla, D. S. Babu, R. V. Babu, and V. M. Patel, “Learning to count in the crowd from limited labeled data,” in ECCV , 2020
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J. Wan and A. Chan, “Modeling noisy annotations for crowd counting,” NIPS , 2020
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H. Lin, Z. Ma, X. Hong, Y. Wang, and Z. Su, “Semi-supervised crowd counting via density agency,” in ACM MM , 2022
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W. Liu, N. Durasov, and P. Fua, “Leveraging self-supervision for cross-domain crowd counting,” in CVPR , 2022, pp. 5341–5352
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W. Lin and A. B. Chan, “Optimal transport minimization: Crowd localization on density maps for semi-supervised counting,” in CVPR , 2023, pp. 21 663–21 673
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