  Human-Object Interactions (HOI) detection, which aims to localize a human and
a relevant object while recognizing their interaction, is crucial for
understanding a still image. Recently, transformer-based models have
significantly advanced the progress of HOI detection. However, the capability
of these models has not been fully explored since the Object Query of the model
is always simply initialized as just zeros, which would affect the performance.
In this paper, we try to study the issue of promoting transformer-based HOI
detectors by initializing the Object Query with category-aware semantic
information. To this end, we innovatively propose the Category-Aware
Transformer Network (CATN). Specifically, the Object Query would be initialized
via category priors represented by an external object detection model to yield
better performance. Moreover, such category priors can be further used for
enhancing the representation ability of features via the attention mechanism.
We have firstly verified our idea via the Oracle experiment by initializing the
Object Query with the groundtruth category information. And then extensive
experiments have been conducted to show that a HOI detection model equipped
with our idea outperforms the baseline by a large margin to achieve a new
state-of-the-art result.
