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TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification

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成果类型:
期刊论文
作者:
Hai Liu;Cheng Zhang;Yongjian Deng;Bochen Xie;Tingting Liu;...
作者机构:
[Hai Liu; Cheng Zhang; Zhaoli Zhang] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
[Yongjian Deng] College of Computer Science, Beijing University of Technology, Beijing, China
Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong
City University of Hong Kong Shenzhen Research Institute, Shenzhen, China
School of Education, Hubei University, Wuhan, Hubei, China
语种:
英文
期刊:
IEEE Transactions on Multimedia
ISSN:
1520-9210
年:
2023
页码:
1-14
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62211530433, 62277041, 62203024, 62177018, 62177019, 62173286, 92167102, 62077020 and 62005092) Research Grants Council of Hong Kong (Grant Number: 9043323, CityU and 11213420) Science and Technology Development Fund, Macau 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: CCNU20ZT017 and CCNU2020ZN008)
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird observation and protection but also a prevalent task for image classification in multimedia processing and computer vision. However, FBIC suffers from several challenges, such as bird molting, complex background, and arbitrary bird posture. To effectively tackle these challenges, we present a novel invariant cues-aware feature concentration Transformer (TransIFC), which learns invariant and core information in bird images. To this end, two novel modules are proposed to leverage the characteristics of bird ...

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