Using Explanations to Improve Ensembling of Visual Question Answering Systems

By Nazneen Fatema Rajani and Raymond J. Mooney. 2017

12 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. Longo Luca
2. Giulia Vilone
3. Xinyi Zhou

Citations in the corpus

• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “Moeyersoms, et al., 2016; Nguyen, Yosinski, and Clune, 2015; Papernot et al., 2017; Rajani and Mooney, 2017; Reed, Akata, Lee, and Schiele, 2016; Sadeghi, Kumar Divvala, and Farhadi, 2015; Samek et al., 2017; Schank, 2013; Schmidhuber, 2015; Shwartz-Ziv and Tishby, 2017; Simonyan, Vedaldi, and…”
• Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “Stacking with Auxiliary Features (SWAF) [197] utilizes heat-maps generated by GradCam to interpret and improve stacked ensembles for visual question answering (VQA) tasks.”