  The small objects in images and videos are usually not independent
individuals. Instead, they more or less present some semantic and spatial
layout relationships with each other. Modeling and inferring such intrinsic
relationships can thereby be beneficial for small object detection. In this
paper, we propose a novel context reasoning approach for small object detection
which models and infers the intrinsic semantic and spatial layout relationships
between objects. Specifically, we first construct a semantic module to model
the sparse semantic relationships based on the initial regional features, and a
spatial layout module to model the sparse spatial layout relationships based on
their position and shape information, respectively. Both of them are then fed
into a context reasoning module for integrating the contextual information with
respect to the objects and their relationships, which is further fused with the
original regional visual features for classification and regression.
Experimental results reveal that the proposed approach can effectively boost
the small object detection performance.
