sal4ahm/RealCQA

Citation text from the card

@InProceedings{10.1007/978-3-031-41682-8_5,
author="Ahmed, Saleem
and Jawade, Bhavin
and Pandey, Shubham
and Setlur, Srirangaraj
and Govindaraju, Venu",
editor="Fink, Gernot A.
and Jain, Rajiv
and Kise, Koichi
and Zanibbi, Richard",
title="RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic",
booktitle="Document Analysis and Recognition - ICDAR 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="66--83",
abstract="We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. Despite efforts to tackle this problem using synthetic charts, solutions are limited by the shortage of annotated real-world data. To fill this gap, we introduce a benchmark and dataset for chart visual QA on real-world charts, offering a systematic analysis of the task and a novel taxonomy for template-based chart question creation. Our contribution includes the introduction of a new answer type, `list', with both ranked and unranked variations. Our study is conducted on a real-world chart dataset from scientific literature, showcasing higher visual complexity compared to other works. Our focus is on template-based QA and how it can serve as a standard for evaluating the first-order logic capabilities of models. The results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust eva

All datasets marked Permissive or share-alike | JSON