We utilized a deep Reinforcement learning framework to sequentially generate a scene graph for an input image. New idea: entire partial graph is encoded as ...

May 26, 2019 · In this work, we propose a new deep generative architecture, called Dynamic Gated Graph Neural Networks (D-GGNN), to perform this inference.

The scene graph is built using a deep reinforcement learning framework: states are partial graphs, encoded using a GGNN, actions choose labels for node and ...

Allamanis, M., Brockschmidt, M., Khademi, M.: Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 (2017); Battaglia, P.W., et al.

Graphs neural networks seem to be a natural choice for generating such scene graphs. ... scene graph generation ... Schulte, “Dynamic gated graph neural networks ...

We propose a new algorithm, called Deep Generative Prob- abilistic Graph Neural Networks (DG-PGNN), to generate a scene graph for an image.

In this work, we introduce a new algorithm for ana- lyzing a diagram, which contains visual and textual infor- mation in an abstract and integrated way.

Apr 2, 2020 · In this work, we translate the scene graph into an Attentive Gated Graph Neural Network which can propagate a message by visual relationship ...

Dynamic scene graph generation (SGG) from videos requires not only a comprehensive understanding of objects across scenes but also a method to capture the ...

Graph Neural Networks (GNNs) are a family of graph networks inspired by ... Dynamic Gated Graph Neural Networks for Scene Graph Generation · M. KhademiO ...