The DOI matches its Crossref record; 165 of the 165 references it cites were found in a registry or an index.
Fine-Grained Image Analysis With Deep Learning: A Survey
Open accessAbstract
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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Cited by
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Metadata
- Authors
- Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-12-01
- Volume
- 44
- Issue
- 12
- Pages
- 8927-8948
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3213192039
- Cited by
- 434
- References
- 165
- FWCI
- 17.02
- Type
- journal article
- Language
- en
- Open access
- green
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- Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S. (2022). Fine-Grained Image Analysis With Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2021.3126648
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