Papers › A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection
A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection
Manli Zhu, Edmond S. L. Ho, Hubert P. H. Shum
Detecting human-object interactions is essential for comprehensive understanding of visual scenes. In particular, spatial connections between humans and objects are important cues for reasoning interactions. To this end, we propose a skeleton-aware graph convolutional network for human-object interaction detection, named SGCN4HOI. Our network exploits the spatial connections between human keypoints and object keypoints to capture their fine-grained structural interactions via graph convolutions. It fuses such geometric features with visual features and spatial configuration features obtained from human-object pairs. Furthermore, to better preserve the object structural information and facilitate human-object interaction detection, we propose a novel skeleton-based object keypoints representation. The performance of SGCN4HOI is evaluated in the public benchmark V-COCO dataset. Experimental results show that the proposed approach outperforms the state-of-the-art pose-based models and achieves competitive performance against other models.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human-Object Interaction Detection | V-COCO | SGCN4HOI | AP(S1) | 53.1 | #22 of 34 | Archive leaderboard | report |
| Human-Object Interaction Detection | V-COCO | SGCN4HOI | AP(S2) | 57.9 | #22 of 34 | Archive leaderboard | report |
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