  We introduce a new scene graph generation method called image-level
attentional context modeling (ILAC). Our model includes an attentional graph
network that effectively propagates contextual information across the graph
using image-level features. Whereas previous works use an object-centric
context, we build an image-level context agent to encode the scene properties.
The proposed method comprises a single-stream network that iteratively refines
the scene graph with a nested graph neural network. We demonstrate that our
approach achieves competitive performance with the state-of-the-art for scene
graph generation on the Visual Genome dataset, while requiring fewer parameters
than other methods. We also show that ILAC can improve regular object detectors
by incorporating relational image-level information.
