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A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection

Zhu, Manli; Ho, Edmund S.L.; Shum, Hubert P.H.

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Manli Zhu

Edmund S.L. Ho


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-theart pose-based models and achieves competitive performance against other models.


Zhu, M., Ho, E. S., & Shum, H. P. (2022). A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection. .

Conference Name IEEE SMC 2022: International Conference on Systems, Man, and Cybernetics
Conference Location Prague, Czech Republic
Start Date Oct 9, 2022
End Date Oct 12, 2022
Acceptance Date Jul 6, 2022
Online Publication Date Nov 18, 2022
Publication Date 2022
Deposit Date Jul 11, 2022
Publicly Available Date Oct 13, 2022
Series ISSN 1062-922X,2577-1655


Accepted Conference Proceeding (988 Kb)

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