论文标题
HOI分析：整合和分解人类对象的相互作用
HOI Analysis: Integrating and Decomposing Human-Object Interaction
论文作者
论文摘要
人类对象相互作用（HOI）由人，对象和隐式相互作用/动词组成。与以前的方法不同的方法将像素直接映射到HOI语义，我们提出了一种以分析方式进行HOI学习的新观点。与谐波分析相比，我们的目标是研究如何用基本波的叠加来表示信号，我们提出了HOI分析。我们认为连贯的HOI可以分解为孤立的人和物体。同时，孤立的人和物体也可以再次集成到连贯的HOI中。此外，与集成和分解相同的人体对象对之间的转换也可以更轻松地进行。结果，隐式动词将在转换函数空间中表示。鉴于此，我们提出了一个集成分解网络（IDN），以实现上述转换并在广泛使用的HOI检测基准上实现最新性能。代码可从https://github.com/dirtyharrylyl/hake-action-torch/tree/idn--（integrating-decomposing-network获得。
Human-Object Interaction (HOI) consists of human, object and implicit interaction/verb. Different from previous methods that directly map pixels to HOI semantics, we propose a novel perspective for HOI learning in an analytical manner. In analogy to Harmonic Analysis, whose goal is to study how to represent the signals with the superposition of basic waves, we propose the HOI Analysis. We argue that coherent HOI can be decomposed into isolated human and object. Meanwhile, isolated human and object can also be integrated into coherent HOI again. Moreover, transformations between human-object pairs with the same HOI can also be easier approached with integration and decomposition. As a result, the implicit verb will be represented in the transformation function space. In light of this, we propose an Integration-Decomposition Network (IDN) to implement the above transformations and achieve state-of-the-art performance on widely-used HOI detection benchmarks. Code is available at https://github.com/DirtyHarryLYL/HAKE-Action-Torch/tree/IDN-(Integrating-Decomposing-Network).
