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Learning to Navigate for Fine-Grained Classification

Open access
Yang, Ze, Luo, Tiange, Wang, Dong, Hu, Zhiqiang, Gao, Jun, Wang, Liwei·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-01264-9_26
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Cited by

  • Multi-attentional Deepfake Detection

    Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019

    “Fine-grained classification [50, 49, 13, 37, 12, 52, 47, 17, 10] is a challenging research task in computer vision, which captures the local discriminative features to distinguish different fine-grained categories.”
    Background

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Metadata

Authors
Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., & Wang, L.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
438-454
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-01264-9_26
OpenAlex
W2891951760
Cited by
616
References
11
FWCI
18.77
Type
conference paper
Language
en
Open access
green
Licence

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CoChat. (2026). Citation check: Learning to Navigate for Fine-Grained Classification. https://app.cochat.ai/papers/10.1007/978-3-030-01264-9_26
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Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., & Wang, L. (2018). Learning to Navigate for Fine-Grained Classification. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01264-9_26

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