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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3126668
|2 doi
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|a pubmed24n1109.xml
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|a eng
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| 100 |
1 |
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|a Du, Ruoyi
|e verfasserin
|4 aut
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| 245 |
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|a Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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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 visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumption that fine-grained information naturally rests within the parts. In this paper, we take a different stance, and show that part operations are not strictly necessary - the key lies with encouraging the network to learn at different granularities and progressively fusing multi-granularity features together. In particular, we propose: (i) a progressive training strategy that effectively fuses features from different granularities, and (ii) a consistent block convolution that encourages the network to learn the category-consistent features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and demonstrate the proposed method consistently outperforms existing alternatives or delivers competitive results. Codes are available at https://github.com/PRIS-CV/PMG-V2
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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 Xie, Jiyang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ma, Zhanyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chang, Dongliang
|e verfasserin
|4 aut
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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 Guo, Jun
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 12 vom: 09. Dez., Seite 9521-9535
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|x 1939-3539
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|g volume:44
|g year:2022
|g number:12
|g day:09
|g month:12
|g pages:9521-9535
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|u http://dx.doi.org/10.1109/TPAMI.2021.3126668
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