.png){kind=icon}
返回
Modelling relations with prototypes for visual relation detection
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-020-09001-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Relations between objects drive our understanding of images. Modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of natural language. This paper tackles the task of predicting relationships in the form of (subject, relation, object) triplets from still images. To address these issues, we propose a framework for learning relation prototypes that aims to capture the complex nature of relation distributions. Concurrently, a network is trained to define a space in which relationship triplets with similar spatial layouts, interacting objects and relations are clustered together. Finally, the network is compared to two models explicitly tackling the problem of synonymy among relations. For this, two well known scene-graph labelling benchmarks are used for training and testing: VRD and Visual Genome. Prediction of relations based on distance to prototype provides a significant increase in the diversity of predicted relations, improving the average relation recall from 40.3% to 41.7% on the first and 31.3% to 35.4% on the second.
Keyword:
Visual relation detection
Prototype
Metric learning
Nearest neighbors
Synonyms
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
PhIP-Seq Reveals Autoantibodies for Ubiquitously Expressed Antigens in Viral Myocarditis.png)
Biology
OAAI
BiologyIF0
OAAI分享收藏Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations视觉基因组: 使用众包密集图像注释连接语言和视觉
OAAI
OAAI分享收藏分享收藏
查看更多内容