Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting

Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label al...

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Bibliographic Details
Main Authors: LIU, Yongtuo, REN, Sucheng, CHAI, Liangyu, WU, Hanjie, XU, Dan, QIN, Jing, HE, Shengfeng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
Crowd counting
Features extraction
Head
Labelings
Semi-supervised
Semi-supervised learning
Spatial labeling redundancy
Technological innovation
Termination of employment
Databases and Information Systems
Technology and Innovation
Online Access:https://ink.library.smu.edu.sg/sis_research/8435
https://ink.library.smu.edu.sg/context/sis_research/article/9438/viewcontent/Reducing_Spatial_Labeling_Redundancy_for_Active_Semi_Supervised_Crowd_Counting__1_.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/8435
https://ink.library.smu.edu.sg/context/sis_research/article/9438/viewcontent/Reducing_Spatial_Labeling_Redundancy_for_Active_Semi_Supervised_Crowd_Counting__1_.pdf

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