  We propose HOI Transformer to tackle human object interaction (HOI) detection
in an end-to-end manner. Current approaches either decouple HOI task into
separated stages of object detection and interaction classification or
introduce surrogate interaction problem. In contrast, our method, named HOI
Transformer, streamlines the HOI pipeline by eliminating the need for many
hand-designed components. HOI Transformer reasons about the relations of
objects and humans from global image context and directly predicts HOI
instances in parallel. A quintuple matching loss is introduced to force HOI
predictions in a unified way. Our method is conceptually much simpler and
demonstrates improved accuracy. Without bells and whistles, HOI Transformer
achieves $26.61\% $ $ AP $ on HICO-DET and $52.9\%$ $AP_{role}$ on V-COCO,
surpassing previous methods with the advantage of being much simpler. We hope
our approach will serve as a simple and effective alternative for HOI tasks.
Code is available at https://github.com/bbepoch/HoiTransformer .
