首页|Parsing Objects at a Finer Granularity:A Survey

Parsing Objects at a Finer Granularity:A Survey

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Fine-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 techno-logies.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 ad-vanced work from a new perspective of learning the part relationship.In this perspective,we first consolidate recent research and bench-mark 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.

Finer granularityvisual parsingpart segmentationfine-grained object recognitionpart relationship

Yifan Zhao、Jia Li、Yonghong Tian

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School of Computer Science,Peking University,Beijing 100871,China

State Key Laboratory of Virtual Reality Technology and Systems,School of Computer Science and Engineering,Beihang University,Beijing 100191,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaKey-Area Research and Development Program of Guangdong Province,ChinaChina Postdoctoral Science Foundation

6213200261825101622020102021B01014000022022M710212

2024

10.1007/s11633-022-1404-6

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(3)
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