A Question-Answering framework for plots using Deep learning
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Summary
A deep learning model is described that addresses the reasoning task of question-answering on bar graphs and pie charts by introducing a novel architecture that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values.
- Type
- preprint
- Published
- 2018-06-12
- Cited by
- 3
- References
- 19
- Access
- Open access
- OpenAlex
- https://openalex.org/W2807921968
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:48357668
Keywords
Computer science, Question answering, Baseline (sea), Artificial intelligence, Deep learning
References
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- Long Short-Term Memory
- The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
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- Ask Your Neurons: A Deep Learning Approach to Visual Question Answering
- Neural Module Networks
- Xception: Deep Learning with Depthwise Separable Convolutions
- Dual Attention Networks for Multimodal Reasoning and Matching
- CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
- MUTAN: Multimodal Tucker Fusion for Visual Question Answering
- A simple neural network module for relational reasoning
- FiLM: Visual Reasoning with a General Conditioning Layer
- FigureQA: An Annotated Figure Dataset for Visual Reasoning
- Learning to Reason: End-to-End Module Networks for Visual Question Answering
- Why Does Unsupervised Pre-training Help Deep Learning?
- Why Does Unsupervised Pre-training Help Deep Learning?
- Exploring Models and Data for Image Question Answering
- Neural Machine Translation by Jointly Learning to Align and Translate
- Microsoft COCO: Common Objects in Context
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