  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).
