Fine-Grained Image Analysis with Deep Learning: A Survey Article Swipe
Xiu-Shen Wei
,
Yi-Zhe Song
,
Oisin Mac Aodha
,
Jianxin Wu
,
Yuxin Peng
,
Jinhui Tang
,
Jian Yang
,
Serge Belongie
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.06119
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.06119
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.
Related Topics
Concepts
Computer science
Benchmark (surveying)
Deep learning
Data science
Class (philosophy)
Field (mathematics)
Artificial intelligence
Task (project management)
Image (mathematics)
Set (abstract data type)
Domain (mathematical analysis)
Key (lock)
Machine learning
Open research
Variation (astronomy)
Cartography
Geography
World Wide Web
Economics
Astrophysics
Mathematical analysis
Pure mathematics
Programming language
Mathematics
Management
Computer security
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.06119
- https://arxiv.org/pdf/2111.06119
- OA Status
- green
- Cited By
- 8
- References
- 192
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212620499
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3212620499Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2111.06119Digital Object Identifier
- Title
-
Fine-Grained Image Analysis with Deep Learning: A SurveyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-11Full publication date if available
- Authors
-
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, Serge BelongieList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.06119Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.06119Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2111.06119Direct OA link when available
- Concepts
-
Computer science, Benchmark (surveying), Deep learning, Data science, Class (philosophy), Field (mathematics), Artificial intelligence, Task (project management), Image (mathematics), Set (abstract data type), Domain (mathematical analysis), Key (lock), Machine learning, Open research, Variation (astronomy), Cartography, Geography, World Wide Web, Economics, Astrophysics, Mathematical analysis, Pure mathematics, Programming language, Mathematics, Management, Computer security, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 5Per-year citation counts (last 5 years)
- References (count)
-
192Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2021-11-11 |
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