Fine-Grained Image Analysis with Deep Learning: A Survey
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Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, which 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.
Originalsprog | Engelsk |
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Tidsskrift | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Vol/bind | 44 |
Udgave nummer | 12 |
Sider (fra-til) | 8927-8948 |
ISSN | 0162-8828 |
DOI | |
Status | Udgivet - 2022 |
Bibliografisk note
Publisher Copyright:
IEEE
ID: 301817074