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.

本文提出了一种名为图像级别关注上下文建模（ILAC）的新场景图生成方法，旨在建立一个基于图像级别特征的关注图网络，从而有效地在图形间传播上下文信息。该方法使用单流网络迭代地优化场景图，展现了与基于物体的上下文相比有更好的性能，在 Visual Genome 数据集上表现优异，需要的参数比其他方法少，并且在结合关系图像级别信息后，还可以提高常规物体探测器的性能。

使用嵌套图神经网络进行图像级别的关注上下文建模