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.

本研究提出了一种名为 SGCN4HOI 的骨架感知图卷积网络，能够通过图卷积捕捉人体骨骼关键点与物体关键点之间的细粒度结构化交互，并使用从人物 - 物品对中获取的几何特征与视觉特征和空间配置特征，进一步融合这些几何特征，以便更好地保留对象结构信息并促进人 - 对象交互检测。在公共基准 V-COCO 数据集上，该方法的性能优于基于姿态的模型的最新技术并获得了与其他模型的有竞争力的性能。