人物交互检测研究进展综述

作为目标检测、行为识别、视觉关系检测的交叉学科，人物交互（human-object?interaction，HOI）检测旨在识别特定应用场景下人与物体的相互关系. 本文对基于图像的人物交互检测研究成果进行了系统总结及论述. 首先，从交互关系建模的原理出发，把人物交互检测方法分为基于全局实例和基于局部实例两类，并对代表性方法进行了详细阐述和分析；进而，根据所采用视觉特征的差异将基于全局实例的方法进行细分，包括融合空间位置信息、融合外观信息与融合人体姿态信息；然后，讨论了零样本学习、弱监督学习以及Transformer模型在人物交互检测中的应用；最后，从交互类别、视觉干扰以及运动视角三方面出发，总结了人物交互检测面临的挑战，并指出领域泛化、实时检测和端到端网络是未来发展的趋势. 

A Survey of Human-Object Interaction Detection

As an interdisciplinary subject of object detection, action recognition and visual relationship detection, human-object interaction (HOI) detection aims to identify the interaction between humans and objects in specific application scenarios. Here, recent work in the field of image-based HOI detection is systematically summarized. Firstly, based on the theory of interaction modeling, HOI detection methods can be divided into two categories: global instance based and local instance based, and the representative methods are elaborated and analyzed in detail. Further, according to the differences in visual features, the methods based on the global instance are further subdivided into fusion of spatial information, fusion of appearance information and fusion of body posture information. Finally, the applications of zero-shot learning, weakly supervised learning and Transformer model in HOI detection are discussed. From three aspects of HOI, visual distraction and motion perspective, the challenges faced by HOI detection are listed, and it is pointed out that domain generalization, real-time detection and end-to-end network are the future development trends.