  Contextual information, such as the co-occurrence of objects and the spatial
and relative size among objects provides deep and complex information about
scenes. It also can play an important role in improving object detection. In
this work, we present two contextual models (rescoring and re-labeling models)
that leverage contextual information (16 contextual relationships are applied
in this paper) to enhance the state-of-the-art RCNN-based object detection
(Faster RCNN). We experimentally demonstrate that our models lead to
enhancement in detection performance using the most common dataset used in this
field (MSCOCO).
