Scene Graph Generation by Iterative Message Passing


Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. The model solves the scene graph inference problem using standard RNNs and learns to iteratively improves its predictions via message passing. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods on generating scene graphs using Visual Genome dataset and inferring support relations with NYU Depth v2 dataset.


  title={Scene Graph Generation by Iterative Message Passing},
  author={Xu, Danfei and Zhu, Yuke and Choy, Christopher and Fei-Fei, Li},
  booktitle={Computer Vision and Pattern Recognition (CVPR)},


This work was supported partially by a Yahoo Labs Macro award and an ONR MURI.