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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3126648
|2 doi
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|a pubmed24n1109.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Wei, Xiu-Shen
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Fine-Grained Image Analysis With Deep Learning
|b A Survey
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| 264 |
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1 |
|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 09.11.2022
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas - fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Song, Yi-Zhe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Aodha, Oisin Mac
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Jianxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Peng, Yuxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tang, Jinhui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Belongie, Serge
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 12 vom: 09. Dez., Seite 8927-8948
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:12
|g day:09
|g month:12
|g pages:8927-8948
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3126648
|3 Volltext
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