Hybrid Perturbation Strategy for Semi-Supervised Crowd Counting-Reference-Cited by-同舟云学术

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Hybrid Perturbation Strategy for Semi-Supervised Crowd Counting

Published:2024 Issue: Volume:33 Page:1227-1240
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Wang Xin¹^ORCID,Zhan Yue¹,Zhao Yang²^ORCID,Yang Tangwen¹^ORCID,Ruan Qiuqi¹

Affiliation:

1. Institute of Information Science and the Beijing Key Laboratory of Advanced Information Science and Network Technology, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Funder

State’s Key Project of Research and Development Plan

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/83/10346232/10430107.pdf?arnumber=10430107

Reference60 articles.

1. CNN-based density estimation and crowd counting: A survey;Gao;arXiv:2003.12783,2020

2. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

3. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

4. PaDNet: Pan-Density Crowd Counting

5. Feature-Aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance

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