Feature Pyramid Attention Model and Multi-Label Focal Loss for Pedestrian Attribute Recognition-Reference-Cited by-同舟云学术

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Feature Pyramid Attention Model and Multi-Label Focal Loss for Pedestrian Attribute Recognition

Published:2020 Issue: Volume:8 Page:164570-164579
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Li Ye^ORCID,Shi Fangyan^ORCID,Hou Shaoqi^ORCID,Li Jipeng^ORCID,Li Chao^ORCID,Yin Guangqiang^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09144234.pdf?arnumber=9144234

Cited by 9 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. SSPNet: Scale and spatial priors guided generalizable and interpretable pedestrian attribute recognition;Pattern Recognition;2024-04

2. Real-Time Human Tracking Using Multi-Features Visual With CNN-LSTM and Q-Learning;IEEE Access;2024

3. Attribute-wise reasoning reinforcement learning for pedestrian attribute retrieval;International Journal of Multimedia Information Retrieval;2023-10-28

4. Exploring attribute localization and correlation for pedestrian attribute recognition;Neurocomputing;2023-04

5. Latent Dynamic Token Vision Transformer for Pedestrian Attribute Recognition;2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta);2022-12

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