Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs

By Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto Gonzalez-Sanchez, Roberto Javier López-Sastre. 2017

40 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Sergey Levine
3. Ruslan Salakhutdinov
4. Trevor Darrell
5. Zhe Gan
6. Abhinav Kumar Gupta
7. Mohit Bansal
8. Kyunghyun Cho

Citations in the corpus

• Relational inductive biases, deep learning, and graph networks (2018, background): “…2018) and multi-agent systems (Sukhbaatar et al., 2016; Hoshen, 2017; Kipf et al., 2018), to reason about knowledge graphs (Bordes et al., 2013; O˜noro-Rubio et al., 2017; Hamaguchi et al., 2017), to predict the chemical properties of molecules (Duvenaud et al., 2015; Gilmer et al., 2017), to…”
• Multi-Modal Knowledge Graph Construction and Application: A Survey (2022, background): “IMAGEgraph [27] proposes to express the relation prediction between unseen images and multi-relational image retrieval as visual-relational queries, such that these queries could be leveraged for MMKG completion.”