Semi-Supervised Crowd Counting With Spatial Temporal Consistency and Pseudo-Label Filter-Reference-Cited by-同舟云学术

Semi-Supervised Crowd Counting With Spatial Temporal Consistency and Pseudo-Label Filter

Published:2023-08 Issue:8 Volume:33 Page:4190-4203
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

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

Affiliation:

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

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

Funder

Fundamental Research Funds for the Central Universities

State’s Key Project of Research and Development Plan

National Natural Science Foundation of China

China Post-Doctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10207864/10032602.pdf?arnumber=10032602

Reference59 articles.

1. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks;lee;Proc Int Conf Mach Learn (ICML),2013

2. Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting

3. Almost Unsupervised Learning for Dense Crowd Counting

4. Scale Aggregation Network for Accurate and Efficient Crowd Counting

5. Unsupervised data augmentation for consistency training;xie;Proc NIPS,2020

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