  Human-object interaction detection is a relatively new task in the world of
computer vision and visual semantic information extraction. With the goal of
machines identifying interactions that humans perform on objects, there are
many real-world use cases for the research in this field. To our knowledge,
this is the first general survey of the state-of-the-art and milestone works in
this field. We provide a basic survey of the developments in the field of
human-object interaction detection. Many works in this field use multi-stream
convolutional neural network architectures, which combine features from
multiple sources in the input image. Most commonly these are the humans and
objects in question, as well as the spatial quality of the two. As far as we
are aware, there have not been in-depth studies performed that look into the
performance of each component individually. In order to provide insight to
future researchers, we perform an individualized study that examines the
performance of each component of a multi-stream convolutional neural network
architecture for human-object interaction detection. Specifically, we examine
the HORCNN architecture as it is a foundational work in the field. In addition,
we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the
field of human-object interaction detection. Code and papers can be found at
https://github.com/SHI-Labs/Human-Object-Interaction-Detection.
