Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

Daesik Kim, YoungJoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating
more » ... nowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications.
doi:10.1109/cvpr.2018.00438 dblp:conf/cvpr/KimYKLK18 fatcat:j5gyp63swzf35aclv2dtxzv4i4