I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
Abstract
In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model.
Keywords
Cite
@article{arxiv.2206.10892,
title = {I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation},
author = {Yiwei Ding and Wenjin Deng and Yinglin Zheng and Pengfei Liu and Meihong Wang and Xuan Cheng and Jianmin Bao and Dong Chen and Ming Zeng},
journal= {arXiv preprint arXiv:2206.10892},
year = {2022}
}
Comments
Accepected by IJCAI 2022