Efficient Crowd Counting via Dual Knowledge Distillation-Reference-Cited by-同舟云学术

Efficient Crowd Counting via Dual Knowledge Distillation

Published:2024 Issue: Volume:33 Page:569-583
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Wang Rui¹^ORCID,Hao Yixue¹^ORCID,Hu Long¹^ORCID,Li Xianzhi¹^ORCID,Chen Min²^ORCID,Miao Yiming³^ORCID,Humar Iztok⁴^ORCID

Affiliation:

1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

3. Shenzhen Institute of Artiffcial Intelligence and Robotics for Society (AIRS), School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong, P.R. China

4. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Shenzhen Institute of Artificial Intelligence and Robotics for Society

Slovenian Research and Innovation Agency through the Decentralized solutions for the Digitalization of Industry and Smart Cities and Communities under Research Program P2-0425

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/10346232/10371216.pdf?arnumber=10371216

Reference74 articles.

1. Redesigning Multi-Scale Neural Network for Crowd Counting

2. Boosting Crowd Counting via Multifaceted Attention

3. Fine-Grained Crowd Counting

4. Decoupled Two-Stage Crowd Counting and Beyond

5. CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model

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