Graph-Based Social Relation Reasoning
Wanhua Li, Yueqi Duan, Jiwen Lu, Jianjiang Feng, Jie Zhou

TL;DR
This paper introduces GR2N, a graph-based method for social relation recognition in images that jointly infers relations, improving accuracy and efficiency by modeling logical constraints among social relations.
Contribution
The paper presents a novel graph relational reasoning network that constructs multiple virtual relation graphs to better capture social relation patterns.
Findings
Improves social relation recognition accuracy.
Enhances efficiency over existing methods.
Generates consistent social relation graphs.
Abstract
Human beings are fundamentally sociable -- that we generally organize our social lives in terms of relations with other people. Understanding social relations from an image has great potential for intelligent systems such as social chatbots and personal assistants. In this paper, we propose a simpler, faster, and more accurate method named graph relational reasoning network (GR2N) for social relation recognition. Different from existing methods which process all social relations on an image independently, our method considers the paradigm of jointly inferring the relations by constructing a social relation graph. Furthermore, the proposed GR2N constructs several virtual relation graphs to explicitly grasp the strong logical constraints among different types of social relations. Experimental results illustrate that our method generates a reasonable and consistent social relation graph…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Human Pose and Action Recognition · Advanced Graph Neural Networks
