Summary of Mmgenbench: Fully Automatically Evaluating Lmms From the Text-to-image Generation Perspective, by Hailang Huang et al.
MMGenBench: Fully Automatically Evaluating LMMs from the Text-to-Image Generation Perspective
by Hailang Huang, Yong Wang, Zixuan Huang, Huaqiu Li, Tongwen Huang, Xiangxiang Chu, Richong Zhang
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The paper proposes a new evaluation pipeline, MMGenBench-Pipeline, to assess the capabilities of Large Multimodal Models (LMMs) in generating detailed descriptions of images. The pipeline involves generating textual descriptions from input images and comparing them with the original images. To test this pipeline, the authors design two benchmarks: MMGenBench-Test, which evaluates LMMs across 13 image patterns, and MMGenBench-Domain, which focuses on generative image performance. A thorough evaluation of over 50 popular LMMs demonstrates the effectiveness and reliability of both the pipeline and benchmark. The results show that many LMMs excelling in existing benchmarks struggle to complete basic tasks related to image understanding and description, indicating a significant potential for performance improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a new way to test how well computers can understand images. Right now, we have ways of testing this, but they’re not perfect. The authors created a new way called MMGenBench-Pipeline that makes it easier and faster to test these computer models. They tested many different models using their pipeline and found that some models that were good at understanding images couldn’t describe them well. This means there’s room for improvement in these models, which could make them even better. |
