  Human-object interaction (HOI) detection for capturing relationships between
humans and objects is an important task in the semantic understanding of
images. When processing human and object keypoints extracted from an image
using a graph convolutional network (GCN) to detect HOI, it is crucial to
extract appropriate object keypoints regardless of the object type and to
design a GCN that accurately captures the spatial relationships between
keypoints. This paper presents the human and object keypoint-based extension
module (HOKEM) as an easy-to-use extension module to improve the accuracy of
the conventional detection models. The proposed object keypoint extraction
method is simple yet accurately represents the shapes of various objects.
Moreover, the proposed human-object adaptive GCN (HO-AGCN), which introduces
adaptive graph optimization and attention mechanism, accurately captures the
spatial relationships between keypoints. Experiments using the HOI dataset,
V-COCO, showed that HOKEM boosted the accuracy of an appearance-based model by
a large margin.
