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FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts

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arxiv 2406.19237 v2 pith:5IIVDVA3 submitted 2024-06-27 cs.CL cs.CVcs.IRcs.LG

classification cs.CLcs.CVcs.IRcs.LG
keywords visualmultimodalreasoningflowvqaansweringbenchmarkflowchartslanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multilingual Multimodal Software Developer for Code Generation

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A 7B vision-language model trained on synthetic diagram-to-code data outperforms several larger open-weight models on a new 10-language UML/flowchart code-generation benchmark.

  2. Exploring Primitive Visual Measurement Understanding and the Role of Output Format in Learning in Vision-Language Models

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Fine-tuning vision-language models with sentence-formatted outputs instead of tuple outputs improves shape attribute and coordinate prediction for larger models, and scaling the loss on numeric tokens sharpens numeric...

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