  Human object interaction (HOI) detection is an important task in image
understanding and reasoning. It is in a form of HOI triplet <human; verb;
object>, requiring bounding boxes for human and object, and action between them
for the task completion. In other words, this task requires strong supervision
for training that is however hard to procure. A natural solution to overcome
this is to pursue weakly-supervised learning, where we only know the presence
of certain HOI triplets in images but their exact location is unknown. Most
weakly-supervised learning methods do not make provision for leveraging data
with strong supervision, when they are available; and indeed a na\"ive
combination of this two paradigms in HOI detection fails to make contributions
to each other. In this regard we propose a mixed-supervised HOI detection
pipeline: thanks to a specific design of momentum-independent learning that
learns seamlessly across these two types of supervision. Moreover, in light of
the annotation insufficiency in mixed supervision, we introduce an HOI element
swapping technique to synthesize diverse and hard negatives across images and
improve the robustness of the model. Our method is evaluated on the challenging
HICO-DET dataset. It performs close to or even better than many
fully-supervised methods by using a mixed amount of strong and weak
annotations; furthermore, it outperforms representative state of the art weakly
and fully-supervised methods under the same supervision.
