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Generic Scene Graph Generation Model with Hierarchical Prompt Learning

文献类型：期刊论文

作者Zhu, Xuhan1,2,3; Xing, Yifei1,2,3; Wang, Ruiping2,3; Wang, Yaowei1; Lan, Xiangyuan1
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2025-06-27
页码19
关键词Generic scene graph generation Hierarchical prompt learning Novel predicate generalization
ISSN号0920-5691
DOI10.1007/s11263-025-02499-z
英文摘要Scene Graph Generation (SGG) delivers structured knowledge to represent complex scenes and has proven effective in many computer vision tasks. However, traditional SGG models suffer from two limitations that hinder their applicability for higher-level visual tasks: (1) a rigid structure that results in low efficiency and limited flexibility, and (2) biased optimization that results in biased predictions that favor uninformative predicates. To resolve these two issues, we propose GSGG (Generic Scene Graph Generation), a novel, efficient, and flexible SGG model that (1) combines generalized modules to construct top-performance and high-efficiency SGG model and (2) employs a prompt learning-based relation decoder with a novel Hierarchical Prompt (HP) learning method to mitigate biased optimization. HP utilizes the composition of basic prompts constrained to progressively narrowed class groups and encourages the corresponding prompts to focus on the learning of increasingly informative predicates. Extensive evaluations on three SGG benchmarks demonstrate the excellent efficiency and performance of GSGG with HP. We also introduce a novel predicate generalization task with a new benchmark, and experiments on it demonstrate the effectiveness of HP in base-to-novel predicate generalization.
资助项目National Natural Science Foundation of China[PCL2023A08] ; Pengcheng Laboratory Research Project[U21B2025] ; Pengcheng Laboratory Research Project[62402252] ; National Natural Science Foundation of China
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001518315700001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/42303]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Ruiping; Lan, Xiangyuan
作者单位1.Pengcheng Lab, Shenzhen 518000, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Xuhan,Xing, Yifei,Wang, Ruiping,et al. Generic Scene Graph Generation Model with Hierarchical Prompt Learning[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2025:19.
APA Zhu, Xuhan,Xing, Yifei,Wang, Ruiping,Wang, Yaowei,&Lan, Xiangyuan.(2025).Generic Scene Graph Generation Model with Hierarchical Prompt Learning.INTERNATIONAL JOURNAL OF COMPUTER VISION,19.
MLA Zhu, Xuhan,et al."Generic Scene Graph Generation Model with Hierarchical Prompt Learning".INTERNATIONAL JOURNAL OF COMPUTER VISION (2025):19.

入库方式： OAI收割

来源：计算技术研究所

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