  Vision and language tasks such as Visual Relation Detection and Visual
Question Answering benefit from semantic features that afford proper grounding
of language. The 3D depth of objects depicted in 2D images is one such feature.
However it is very difficult to obtain accurate depth information without
learning the appropriate features, which are scene dependent. The state of the
art in this area are complex Neural Network models trained on stereo image data
to predict depth per pixel. Fortunately, in some tasks, its only the relative
depth between objects that is required. In this paper the extent to which
semantic features can predict course relative depth is investigated. The
problem is casted as a classification one and geometrical features based on
object bounding boxes, object labels and scene attributes are computed and used
as inputs to pattern recognition models to predict relative depth. i.e behind,
in-front and neutral. The results are compared to those obtained from averaging
the output of the monodepth neural network model, which represents the
state-of-the art. An overall increase of 14% in relative depth accuracy over
relative depth computed from the monodepth model derived results is achieved.
