Papers › Graph-Based Social Relation Reasoning
Graph-Based Social Relation Reasoning
Wanhua Li, Yueqi Duan, Jiwen Lu, Jianjiang Feng, Jie zhou
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Social Relationship Recognition | PIPA | GR2N | Accuracy | 64.3 | #2 of 6 | Archive leaderboard | report |
| Visual Social Relationship Recognition | PIPA | GR2N | Accuracy (domain) | 72.3 | #2 of 6 | Archive leaderboard | report |
| Visual Social Relationship Recognition | PISC | GR2N | mAP | 72.7 | #2 of 5 | Archive leaderboard | report |
| Visual Social Relationship Recognition | PISC | GR2N | mAP (Coarse) | 83.1 | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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