Answering Questions about Data Visualizations using Efficient Bimodal Fusion

Kushal Kafle, Robik Shrestha, Scott Cohen, Brian Price, Christopher Kanan; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1498-1507

Abstract


Chart question answering (CQA) is a newly proposed visual question answering (VQA) task where an algorithm must answer questions about data visualizations, e.g. bar charts, pie charts, and line graphs. CQA requires capabilities that natural-image VQA algorithms lack: fine-grained measurements, optical character recognition, and handling out-of-vocabulary words in both questions and answers. Without modifications, state-of-the-art VQA algorithms perform poorly on this task. Here, we propose a novel CQA algorithm called parallel recurrent fusion of image and language (PReFIL). PReFIL first learns bimodal embeddings by fusing question and image features and then intelligently aggregates these learned embeddings to answer the given question. Despite its simplicity, PReFIL greatly surpasses state-of-the art systems and human baselines on both the FigureQA and DVQA datasets. Additionally, we demonstrate that PReFIL can be used to reconstruct tables by asking a series of questions about a chart.

Related Material


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[bibtex]
@InProceedings{Kafle_2020_WACV,
author = {Kafle, Kushal and Shrestha, Robik and Cohen, Scott and Price, Brian and Kanan, Christopher},
title = {Answering Questions about Data Visualizations using Efficient Bimodal Fusion},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}