DecAug: Augmenting HOI Detection via Decomposition
Keywords:Scene Analysis & Understanding, Video Understanding & Activity Analysis, Language and Vision
AbstractHuman-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 states. 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 HICO-DET 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.
How to Cite
Fang, H.-S., Xie, Y., Shao, D., Li, Y.-L., & Lu, C. (2021). DecAug: Augmenting HOI Detection via Decomposition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 1300-1308. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16218
AAAI Technical Track on Computer Vision I