Summary of A-bench: Are Lmms Masters at Evaluating Ai-generated Images?, by Zicheng Zhang et al.
A-Bench: Are LMMs Masters at Evaluating AI-generated Images?
by Zicheng Zhang, Haoning Wu, Chunyi Li, Yingjie Zhou, Wei Sun, Xiongkuo Min, Zijian Chen, Xiaohong Liu, Weisi Lin, Guangtao Zhai
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the challenge of accurately assessing AI-generated images (AIGIs) using generative models. Researchers have employed large multi-modal models (LMMs) to evaluate AIGIs, but the precision and validity of these models are still unclear. To address this gap, the authors introduce A-Bench, a novel benchmark designed to diagnose whether LMMs can effectively evaluate AIGIs. A-Bench emphasizes both high-level semantic understanding and low-level visual quality perception, utilizing various generative models for AIGI creation and leading LMMs for evaluation. The authors test 2,864 AIGIs from 16 text-to-image models, each paired with question-answers annotated by human experts, across 18 leading LMMs. This benchmark aims to enhance the evaluation process and promote the generation quality of AIGIs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to tell if AI-generated images are good or not. Right now, it’s hard to know because we’re using big models to check them, but those models might not be doing a great job. To fix this problem, the researchers created something called A-Bench that can help us figure out if these big models are working well or not. It looks at both what the image means and how it looks, and uses different AI models to create the images and check them. They tested thousands of images from 16 different AI models with questions and answers written by people, using 18 different big models to see how they did. The goal is to make it easier to tell if AI-generated images are good or not. |
Keywords
» Artificial intelligence » Multi modal » Precision
