AutoScale: Learning to Scale for Crowd Counting

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Cite
Xu, Chenfeng, et al. “AutoScale: Learning to Scale for Crowd Counting”. International Journal of Computer Vision, vol. 130, no. 2, 2022, pp. 405-34, https://doi.org/10.1007/s11263-021-01542-z.
Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., & Tomizuka, M. (2022). AutoScale: Learning to Scale for Crowd Counting. International Journal of Computer Vision, 130(2), 405-434. https://doi.org/10.1007/s11263-021-01542-z
Xu, Chenfeng, Dingkang Liang, Yongchao Xu, Song Bai, Wei Zhan, Xiang Bai, and Masayoshi Tomizuka. “AutoScale: Learning to Scale for Crowd Counting”. International Journal of Computer Vision 130, no. 2 (2022): 405-34. https://doi.org/10.1007/s11263-021-01542-z.
Xu C, Liang D, Xu Y, Bai S, Zhan W, Bai X, et al. AutoScale: Learning to Scale for Crowd Counting. International Journal of Computer Vision. 2022;130(2):405-34.
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Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Technology
Electrical engineering
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Refrences
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10.1109/TCSVT.2018.2837153 2018
10.1109/TPAMI.2016.2644615 2017
Do We Need More Training Data? International Journal of Computer Vision
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 104 2016
10.1109/TPAMI.2015.2396051 2015
10.1109/TKDE.2008.239 2009
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Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science25
Science: Science (General): Cybernetics: Information theory15
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks13
Technology: Engineering (General). Civil engineering (General)11
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software11
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware11
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics10
Technology: Mechanical engineering and machinery7
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication5
Science: Mathematics: Instruments and machines3
Technology: Electrical engineering. Electronics. Nuclear engineering2
Science: Science (General)2
Technology: Technology (General): Industrial engineering. Management engineering: Information technology2
Medicine1
Science1
Science: Mathematics1
Technology: Engineering (General). Civil engineering (General): Transportation engineering1
Technology: Manufactures1
Technology: Technology (General): Industrial engineering. Management engineering1
Agriculture1
Agriculture: Agriculture (General)1
Agriculture: Plant culture1
Technology: Photography1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 25 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A point and density map hybrid network for crowd counting and localization based on unmanned aerial vehicles and was published in 2022. The most recent citation comes from a 2024 study titled PPCL-RSE: Point prediction for counting and localization of litopenaeus vannamei fry with region-based super-resolution enhancement. This article reached its peak citation in 2023, with 14 citations. It has been cited in 28 different journals, 17% of which are open access. Among related journals, the IEEE Access cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year