PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
<|reference_start|>PBADet: A One-Stage Anchor-Free Approach for Part-Body Association: The detection of human parts (e.g., hands, face) and their correct association with individuals is an essential task, e.g., for ubiquitous human-machine interfaces and action recognition. Traditional methods often employ multi-stage processes, rely on cumbersome anchor-based systems, or do not scale well to larger part sets. This paper presents PBADet, a novel one-stage, anchor-free approach for part-body association detection. Building upon the anchor-free object representation across multi-scale feature maps, we introduce a singular part-to-body center offset that effectively encapsulates the relationship between parts and their parent bodies. Our design is inherently versatile and capable of managing multiple parts-to-body associations without compromising on detection accuracy or robustness. Comprehensive experiments on various datasets underscore the efficacy of our approach, which not only outperforms existing state-of-the-art techniques but also offers a more streamlined and efficient solution to the part-body association challenge.<|reference_end|>
@article{gao2024pbadet:,
    title={PBADet: A One-Stage Anchor-Free Approach for Part-Body Association},
    author={Zhongpai Gao, Huayi Zhou, Abhishek Sharma, Meng Zheng, Benjamin
  Planche, Terrence Chen, Ziyan Wu},
    journal={arXiv preprint arXiv:2402.07814},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07814},
    primaryClass={cs.CV cs.AI}
}