  Human-object interaction (HOI) detection requires a large amount of annotated
data. Current algorithms suffer from insufficient training samples and category
imbalance within datasets. To increase data efficiency, in this paper, we
propose an efficient and effective data augmentation method called DecAug for
HOI detection. Based on our proposed object state similarity metric, object
patterns across different HOIs are shared to augment local object appearance
features without changing their state. Further, we shift spatial correlation
between humans and objects to other feasible configurations with the aid of a
pose-guided Gaussian Mixture Model while preserving their interactions.
Experiments show that our method brings up to 3.3 mAP and 1.6 mAP improvements
on V-COCO and HICODET dataset for two advanced models. Specifically,
interactions with fewer samples enjoy more notable improvement. Our method can
be easily integrated into various HOI detection models with negligible extra
computational consumption. Our code will be made publicly available.
