  Human-object interaction (HOI) detection as a downstream of object detection
tasks requires localizing pairs of humans and objects and extracting the
semantic relationships between humans and objects from an image. Recently,
one-stage approaches have become a new trend for this task due to their high
efficiency. However, these approaches focus on detecting possible interaction
points or filtering human-object pairs, ignoring the variability in the
location and size of different objects at spatial scales. To address this
problem, we propose a transformer-based method, QAHOI (Query-Based Anchors for
Human-Object Interaction detection), which leverages a multi-scale architecture
to extract features from different spatial scales and uses query-based anchors
to predict all the elements of an HOI instance. We further investigate that a
powerful backbone significantly increases accuracy for QAHOI, and QAHOI with a
transformer-based backbone outperforms recent state-of-the-art methods by large
margins on the HICO-DET benchmark. The source code is available at
$\href{https://github.com/cjw2021/QAHOI}{\text{this https URL}}$.
