English

Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States

Computer Vision and Pattern Recognition 2020-08-17 v1 Machine Learning Image and Video Processing

Abstract

Existing Human-Object Interaction (HOI) Detection approaches have achieved great progress on nonrare classes while rare HOI classes are still not well-detected. In this paper, we intend to apply human prior knowledge into the existing work. So we add human-labeled rules to PaStaNet and propose Rb-PaStaNet aimed at improving rare HOI classes detection. Our results show a certain improvement of the rare classes, while the non-rare classes and the overall improvement is more considerable.

Keywords

Cite

@article{arxiv.2008.06285,
  title  = {Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States},
  author = {Shenyu Zhang and Zichen Zhu and Qingquan Bao},
  journal= {arXiv preprint arXiv:2008.06285},
  year   = {2020}
}