Purvi Chaurasia
2024
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts
Shubhankar Singh
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Purvi Chaurasia
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Yerram Varun
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Pranshu Pandya
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Vatsal Gupta
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Vivek Gupta
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Dan Roth
Findings of the Association for Computational Linguistics: ACL 2024
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.
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Co-authors
- Shubhankar Singh 1
- Yerram Varun 1
- Pranshu Pandya 1
- Vatsal Gupta 1
- Vivek Gupta 1
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- Dan Roth 1