# Graph-Based Social Relation Reasoning

arXiv: https://arxiv.org/abs/2007.07453 · HTML: https://mlsanity.com/paper/2007.07453/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 14 of 14)

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- [Multipar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations](https://mlsanity.com/paper/2304.12204/) (2023)
- [Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification](https://mlsanity.com/paper/2402.19339/) (2024)
- [SocialFusion: Addressing Social Degradation in Pre-trained Vision-Language Models](https://mlsanity.com/paper/2512.01148/) (2025)
- [Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation](https://mlsanity.com/paper/2107.14425/) (2021)
- [GRITv2: Efficient and Light-weight Social Relation Recognition](https://mlsanity.com/paper/2403.06895/) (2024)
- [From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition](https://mlsanity.com/paper/2406.08358/) (2024)
- [CloSE: A Geometric Shape-Agnostic Cloth State Representation](https://mlsanity.com/paper/2504.05033/) (2025)

## Abstract (arXiv)

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 and improves the performance in both accuracy and efficiency.