Towards using count-level weak supervision for crowd counting

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Cite
Lei, Yinjie, et al. “Towards Using Count-Level Weak Supervision for Crowd Counting”. Pattern Recognition, vol. 109, 2021, p. 107616, https://doi.org/10.1016/j.patcog.2020.107616.
Lei, Y., Liu, Y., Zhang, P., & Liu, L. (2021). Towards using count-level weak supervision for crowd counting. Pattern Recognition, 109, 107616. https://doi.org/10.1016/j.patcog.2020.107616
Lei, Yinjie, Yan Liu, Pingping Zhang, and Lingqiao Liu. “Towards Using Count-Level Weak Supervision for Crowd Counting”. Pattern Recognition 109 (2021): 107616. https://doi.org/10.1016/j.patcog.2020.107616.
Lei Y, Liu Y, Zhang P, Liu L. Towards using count-level weak supervision for crowd counting. Pattern Recognition. 2021;109:107616.
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Refrences
Title Journal Journal Categories Citations Publication Date
Segnet: a deep convolutional encoder-decoder architecture for image segmentation 2017
Large scale crowd analysis based on convolutional neural network Pattern Recognition
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 32 2015
Pedestrian detection: an evaluation of the state of the art 2012
Robust real-time face detection 2004
A system for counting people in video images using neural networks to identify the background scene Pattern Recognition
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 23 1996
Citations
Title Journal Journal Categories Citations Publication Date
Rethinking Global Context in Crowd Counting Machine Intelligence Research
  • Technology: Mechanical engineering and machinery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2024
Privacy-aware crowd counting by decentralized learning with parallel transformers Internet of Things 2024
CrowdTrans: Learning top-down visual perception for crowd counting by transformer Neurocomputing
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2024
Self-distillation and self-supervision for partial label learning Pattern Recognition
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 2 2024
A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration IEEE/CAA Journal of Automatica Sinica
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Mechanical engineering and machinery
  • Technology: Engineering (General). Civil engineering (General)
 2024
Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science27
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics25
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks19
Technology: Mechanical engineering and machinery14
Technology: Engineering (General). Civil engineering (General)13
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software12
Science: Science (General): Cybernetics: Information theory10
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware8
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication4
Technology: Electrical engineering. Electronics. Nuclear engineering3
Science: Physics3
Science: Geology2
Geography. Anthropology. Recreation: Geography (General)2
Technology: Photography2
Medicine2
Science2
Science: Science (General)2
Science: Physics: Optics. Light2
Medicine: Medicine (General): Computer applications to medicine. Medical informatics2
Science: Physics: Acoustics. Sound1
Technology: Engineering (General). Civil engineering (General): Transportation engineering1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 27 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Speedy Image Crowd Counting by Light Weight Convolutional Neural Network and was published in 2021. The most recent citation comes from a 2024 study titled Rethinking Global Context in Crowd Counting. This article reached its peak citation in 2023, with 21 citations. It has been cited in 29 different journals, 13% of which are open access. Among related journals, the Pattern Recognition cited this research the most, with 4 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year