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Local context attention learning for fine-grained scene graph generation

文献类型：期刊论文

作者Zhu, Xuhan1,2; Wang, Ruiping1,3; Lan, Xiangyuan2; Wang, Yaowei
刊名PATTERN RECOGNITION
出版日期2024-12-01
卷号156页码:13
关键词Fine-grained scene graph generation Local context Location attention network Local context-consistent label transfer
ISSN号0031-3203
DOI10.1016/j.patcog.2024.110708
英文摘要Fine-grained scene graph generation aims to parse the objects and their fine-grained relationships within scenes. Despite the significant progress in recent years, their performance is still limited by two major issues: (1) ambiguous perception under a global view; (2) the lack of reliable, fine-grained annotations. We argue that understanding the local context is important in addressing the two issues. However, previous works often overlook it, which limits their effectiveness in fine-grained scene graph generation. To tackle this challenge, we introduce a Local-context Attention Learning method that concentrates on local context and can generate high-reliability, fine-grained annotations. It comprises two components: (1) The Fine-grained Location Attention Network (FLAN), a multi-branch network that encompasses global and local branches, can attend to local informative context and perceive granularity levels in different regions, thereby adaptively enhancing the learning of fine-grained locations. (2) The Fine-grained Location Label Transfer (FLLT) method identifies coarse-grained labels inconsistent with the local context and determines which labels should be transferred through the global confidence thresholding strategy, finally transferring them to reliable local context-consistent fine-grained ones. Experiments conducted on the Visual Genome, OpenImage, and GQA200 datasets show that the proposed methods achieve significant improvements on the fine-grained scene graph generation task. By addressing the challenge mentioned above, our method also achieves state-of-the-art performances on the three datasets.
资助项目Peng Cheng Laboratory Research, China[PCL2023A08] ; Natural Science Foundation of China[U21B2025]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001267770200001
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/39862]  
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
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Peng Cheng Lab, Shenzhen 518000, 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,Wang, Ruiping,Lan, Xiangyuan,et al. Local context attention learning for fine-grained scene graph generation[J]. PATTERN RECOGNITION,2024,156:13.
APA Zhu, Xuhan,Wang, Ruiping,Lan, Xiangyuan,&Wang, Yaowei.(2024).Local context attention learning for fine-grained scene graph generation.PATTERN RECOGNITION,156,13.
MLA Zhu, Xuhan,et al."Local context attention learning for fine-grained scene graph generation".PATTERN RECOGNITION 156(2024):13.

入库方式： OAI收割

来源：计算技术研究所

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