论文标题
人类对象相互作用检测的骨骼感知图卷积网络
A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection
论文作者
论文摘要
检测人对象相互作用对于全面理解视觉场景至关重要。特别是，人与物体之间的空间连接是推理相互作用的重要提示。为此，我们提出了一个用于人类对象相互作用检测的骨骼感知图卷积网络，称为SGCN4HOI。我们的网络利用了人类关键点和对象关键点之间的空间连接，以通过图卷积捕获其细粒的结构相互作用。它将此类几何特征与视觉特征和空间配置特征融合在一起，并从人类对象对获得。此外，为了更好地保留对象结构信息并促进人类对象的相互作用检测，我们提出了一种新型的基于骨架的对象关键点表示。 SGCN4HOI的性能在公共基准V-Coco数据集中进行了评估。实验结果表明，所提出的方法的表现优于最先进的姿势模型，并针对其他模型实现了竞争性能。
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.
