  The task of Human-Object Interaction (HOI) detection is to detect humans and
their interactions with surrounding objects, where transformer-based methods
show dominant advances currently. However, these methods ignore the
relationship among humans, objects, and interactions: 1) human features are
more contributive than object ones to interaction prediction; 2) interactive
information disturbs the detection of objects but helps human detection. In
this paper, we propose a Human and Object Disentangling Network (HODN) to model
the HOI relationships explicitly, where humans and objects are first detected
by two disentangling decoders independently and then processed by an
interaction decoder. Considering that human features are more contributive to
interaction, we propose a Human-Guide Linking method to make sure the
interaction decoder focuses on the human-centric regions with human features as
the positional embeddings. To handle the opposite influences of interactions on
humans and objects, we propose a Stop-Gradient Mechanism to stop interaction
gradients from optimizing the object detection but to allow them to optimize
the human detection. Our proposed method achieves competitive performance on
both the V-COCO and the HICO-Det datasets. It can be combined with existing
methods easily for state-of-the-art results.
