  Determining which image regions to concentrate on is critical for
Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on
either detected human and object pairs or pre-defined interaction locations,
which limits learning of the effective features. In this paper, we reformulate
HOI detection as an adaptive set prediction problem, with this novel
formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with
parallel instances and interaction branches. To attain this, we map a trainable
interaction query set to an interaction prediction set with a transformer. Each
query adaptively aggregates the interaction-relevant features from global
contexts through multi-head co-attention. Besides, the training process is
supervised adaptively by matching each ground truth with the interaction
prediction. Furthermore, we design an effective instance-aware attention module
to introduce instructive features from the instance branch into the interaction
branch. Our method outperforms previous state-of-the-art methods without any
extra human pose and language features on three challenging HOI detection
datasets. Especially, we achieve over $31\%$ relative improvement on a
large-scale HICO-DET dataset. Code is available at
https://github.com/yoyomimi/AS-Net.
