Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification-Reference-Cited by-同舟云学术

Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Multimedia
language:
Short-container-title:

Author:

Wang Yanan¹,Liao Shengcai¹,Shao Ling²

Affiliation:

1. Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE

2. Inception Institute of Artificial Intelligence (IIAI) & Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

Funder

NSFC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394171.3413815

Reference36 articles.

1. Looking beyond appearances: Synthetic training data for deep CNNs in re-identification

2. Christopher M Bishop. 2006. Pattern recognition and machine learning. Springer. Christopher M Bishop. 2006. Pattern recognition and machine learning. Springer.

3. Christian Chang. 2006. Modeling UV Mapping and Texturing 3D Game Weapons. Wordware Publishing Inc. Christian Chang. 2006. Modeling UV Mapping and Texturing 3D Game Weapons. Wordware Publishing Inc.

4. MakeHuman Community. 2020 a. MakeHuman: Open Source Tool for Making 3D Characters. http://www.makehumancommunity.org. MakeHuman Community. 2020 a. MakeHuman: Open Source Tool for Making 3D Characters. http://www.makehumancommunity.org.

Cited by 58 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Human Image Generation: A Comprehensive Survey;ACM Computing Surveys;2024-06-28

2. Learning Hierarchical Visual Transformation for Domain Generalizable Visual Matching and Recognition;International Journal of Computer Vision;2024-05-27

3. Situational diversity in video person re-identification: introducing MSA-BUPT dataset;Complex & Intelligent Systems;2024-05-23

4. Synthetic Data for Video Surveillance Applications of Computer Vision: A Review;International Journal of Computer Vision;2024-05-17

5. An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification;International Journal of Computer Vision;2024-04-24