计算机科学
深度学习
水准点(测量)
人工智能
班级(哲学)
领域(数学)
机器学习
开放式研究
集合(抽象数据类型)
图像(数学)
任务(项目管理)
领域(数学分析)
数据科学
上下文图像分类
钥匙(锁)
地图学
万维网
地理
经济
管理
程序设计语言
纯数学
数学分析
计算机安全
数学
作者
Xiu-Shen Wei,Yi-Zhe Song,Oisin Mac Aodha,Jianxin Wu,Yuxin Peng,Jinhui Tang,Jian Yang,Serge Belongie
标识
DOI:10.1109/tpami.2021.3126648
摘要
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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