Learning Discriminative Features for Crowd Counting-Reference-Cited by-同舟云学术

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Learning Discriminative Features for Crowd Counting

Published:2024 Issue: Volume:33 Page:3749-3764
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Chen Yuehai¹^ORCID,Wang Qingzhong²^ORCID,Yang Jing¹^ORCID,Chen Badong³^ORCID,Xiong Haoyi²^ORCID,Du Shaoyi⁴^ORCID

Affiliation:

1. School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, China

2. Baidu Research, Beijing, China

3. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shanxi, China

4. Department of Ultrasound, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/83/10346232/10552195.pdf?arnumber=10552195

Reference89 articles.

1. Domain-Adaptive Crowd Counting via High-Quality Image Translation and Density Reconstruction

2. Towards Learning Multi-domain Crowd Counting

3. Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets

4. Fine-Grained Crowd Counting

5. A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

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1. Senet: Superresolution Enhancement Network for Crowd Counting;2024

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