Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. FlowVQA comprises 2,272 carefully generated and human-verified flowchart images from three distinct content sources, along with 22,413 diverse question-answer pairs, to test a spectrum of reasoning tasks, including information localization, decision-making, and logical progression. We conduct a thorough baseline evaluation on a suite of both open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. The results underscore the benchmark's potential as a vital tool for advancing the field of multimodal modeling, providing a focused and challenging environment for enhancing model performance in visual and logical reasoning tasks.
@article{arxiv.2406.19237,
title = {FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts},
author = {Shubhankar Singh and Purvi Chaurasia and Yerram Varun and Pranshu Pandya and Vatsal Gupta and Vivek Gupta and Dan Roth},
journal= {arXiv preprint arXiv:2406.19237},
year = {2024}
}