  Context is important for accurate visual recognition. In this work we propose
an object detection algorithm that not only considers object visual appearance,
but also makes use of two kinds of context including scene contextual
information and object relationships within a single image. Therefore, object
detection is regarded as both a cognition problem and a reasoning problem when
leveraging these structured information. Specifically, this paper formulates
object detection as a problem of graph structure inference, where given an
image the objects are treated as nodes in a graph and relationships between the
objects are modeled as edges in such graph. To this end, we present a so-called
Structure Inference Network (SIN), a detector that incorporates into a typical
detection framework (e.g. Faster R-CNN) with a graphical model which aims to
infer object state. Comprehensive experiments on PASCAL VOC and MS COCO
datasets indicate that scene context and object relationships truly improve the
performance of object detection with more desirable and reasonable outputs.
