  Human-Object Interaction (HOI) detection is the task of identifying a set of
<human, object, interaction> triplets from an image. Recent work proposed
transformer encoder-decoder architectures that successfully eliminated the need
for many hand-designed components in HOI detection through end-to-end training.
However, they are limited to single-scale feature resolution, providing
suboptimal performance in scenes containing humans, objects and their
interactions with vastly different scales and distances. To tackle this
problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered
by two novel HOI-aware deformable attention modules called Dual-Entity
attention and Entity-conditioned Context attention. While existing deformable
attention comes at a huge cost in HOI detection performance, our proposed
attention modules of MSTR learn to effectively attend to sampling points that
are essential to identify interactions. In experiments, we achieve the new
state-of-the-art performance on two HOI detection benchmarks.
