Human-object interaction detection based on graph model

Human-Object Interaction (HOI) detection is a fundamental task for understanding real-world scenes. In this paper, a graph model-based human-object interaction detection algorithm is proposed, which aims to make full use of the visual-spatial features and semantic information of human-object instances in the image, thereby improving the accuracy of interaction detection. Aiming at the characteristics of visual-spatial features and semantic information, we take the visual features of human and object instance boxes as nodes, and the corresponding spatial features of interaction relations as edges to construct an initial dense graph, and adaptively update the graph through the spatial and semantic information of instances. The V-COCO dataset is used to evaluate the algorithm, and the final accuracy is significantly improved, which proves the effectiveness of the algorithm.