# A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection Source: [A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:40:39.056Z Authors: Manli Zhu, Edmond S. L. Ho, Hubert P. H. Shum Published: 2022-07-11T15:20:18.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Human-Object Interaction Detection on V-COCO (test)](): Human-Object Interaction Detection; V-COCO (test); AP (Role, Scenario 1): 53.1. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()