CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Parsing Objects at a Finer Granularity: A Survey
Yifan Zhao1; Jia Li2; Yonghong Tian1
Source PublicationMachine Intelligence Research
AbstractFine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.
KeywordFiner granularity, visual parsing, part segmentation, fine-grained object recognition, part relationship
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Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.School of Computer Science, Peking University, Beijing 100871, China
2.State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Recommended Citation
GB/T 7714
Yifan Zhao,Jia Li,Yonghong Tian. Parsing Objects at a Finer Granularity: A Survey[J]. Machine Intelligence Research,2024,21(3):431-451.
APA Yifan Zhao,Jia Li,&Yonghong Tian.(2024).Parsing Objects at a Finer Granularity: A Survey.Machine Intelligence Research,21(3),431-451.
MLA Yifan Zhao,et al."Parsing Objects at a Finer Granularity: A Survey".Machine Intelligence Research 21.3(2024):431-451.
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