Progressive Graph Reasoning-Based Social Relation Recognition

Wang Tang

doi:10.1109/TMM.2023.3344359

2024-01-01

PRE

DOI：10.1109/TMM.2023.3344359

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摘要

En 中文

Identifying relationships between people from images is essential for studying social activities and interactions, and this has significant potential to further the understanding of human social behaviors. Existing image-based research mainly explores social relationships at the dyadic level, i.e., recognizing pairwise relationships based on visual features of persons, objects, and scenes and their logical constraints. Notably, social relational structures are hierarchically nested, i.e., individuals and dyads are nested within group structures, as indicated in the social relations model (SRM) of social psychology. However, existing computer vision-based studies fail to consider hierarchical nested structures, thus overlooking some of the most important interactions, which leads to poor relation reasoning. To improve the performance of reasoning neural networks, we propose a novel SRM framework for progressive graph reasoning (PGR) to explore social interactions. Specifically, we construct individual-dyad and dyad-group graphs to progressively explore the impact of individuals and groups on recognition of dyadic relationships. A transformer is utilized to fuse visual features and graph reasoning knowledge into a comprehensive representation of social relationships. We demonstrate the effectiveness of the proposed model based on PGR using several public datasets and perform extensive ablation studies to explore the reasons behind its superior performance. Experimental results demonstrate that our proposed model successfully predicts social relationships with higher accuracy than state-of-the-art methods.

Keyword:

Social relation recognition

progressive graph reasoning

social relations model

social psychology

transformer