Summary of Flowvqa: Mapping Multimodal Logic in Visual Question Answering with Flowcharts, by Shubhankar Singh et al.
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts
by Shubhankar Singh, Purvi Chaurasia, Yerram Varun, Pranshu Pandya, Vatsal Gupta, Vivek Gupta, Dan Roth
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces FlowVQA, a novel benchmark for assessing the capabilities of multimodal language models in reasoning with flowcharts as visual contexts. The benchmark comprises 2,272 flowchart images from three content sources, along with 22,413 question-answer pairs, testing various reasoning tasks like information localization and decision-making. The authors conduct baseline evaluations on several multimodal models using different strategies, highlighting the importance of FlowVQA in advancing multimodal modeling. The results demonstrate the benchmark’s potential in enhancing model performance in visual and logical reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test computer models that can understand pictures and answer questions about them. The new test, called FlowVQA, uses flowcharts as a picture to help the models think more logically. The test has many different types of problems for the models to solve, like finding specific information in a picture or making decisions based on what it sees. The researchers tested several computer models using this new test and found that it can really help them improve their ability to understand pictures and make smart choices. |
