  Two-stage methods have dominated Human-Object Interaction (HOI) detection for
several years. Recently, one-stage HOI detection methods have become popular.
In this paper, we aim to explore the essential pros and cons of two-stage and
one-stage methods. With this as the goal, we find that conventional two-stage
methods mainly suffer from positioning positive interactive human-object pairs,
while one-stage methods are challenging to make an appropriate trade-off on
multi-task learning, i.e., object detection, and interaction classification.
Therefore, a core problem is how to take the essence and discard the dregs from
the conventional two types of methods. To this end, we propose a novel
one-stage framework with disentangling human-object detection and interaction
classification in a cascade manner. In detail, we first design a human-object
pair generator based on a state-of-the-art one-stage HOI detector by removing
the interaction classification module or head and then design a relatively
isolated interaction classifier to classify each human-object pair. Two cascade
decoders in our proposed framework can focus on one specific task, detection or
interaction classification. In terms of the specific implementation, we adopt a
transformer-based HOI detector as our base model. The newly introduced
disentangling paradigm outperforms existing methods by a large margin, with a
significant relative mAP gain of 9.32% on HICO-Det. The source codes are
available at https://github.com/YueLiao/CDN.
