Summary of Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel Vqa, by Yue Fan et al.
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA
by Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Xinze Guan, Xin Eric Wang
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The paper introduces Multipanel Visual Question Answering (MultipanelVQA), a novel benchmark to evaluate models in comprehending multipanel images. The dataset consists of 6,600 triplets of questions, answers, and multipanel images that challenge models in understanding complex scenes. The evaluation shows that state-of-the-art Multimodal Large Language Models (MLLMs) struggle with these questions, even though humans can achieve high accuracy. The paper also analyzes the factors affecting MLLMs’ performance using synthetic data and offers insights for enhancement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MultipanelVQA is a new way to test how well artificial intelligence models understand pictures that have multiple parts, like screenshots or posters. The goal is to make better AI systems that can look at these kinds of images and figure out what they mean. Right now, the best AI models are not very good at doing this, even though people can do it easily. To help improve these AI models, researchers created a big dataset with thousands of examples of multipanel images and questions about what’s in those images. This will help make better AI systems that can understand complex scenes. |
