对比7种多模态模型在细粒度图像分析中的表现。
Benchmarking Multimodal Models for Fine-Grained Image Analysis: A Comparative Study Across Diverse Visual Features
- 构建涵盖7类视觉特征的评测基准
- 基于1.4万张图文生成图像测试模型表现
- 帮助选型与改进图像理解模型
本文提出一个用于评估多模态模型在图像分析与理解能力的基准。该基准聚焦于七个关键视觉维度：主体对象、附加对象、背景、细节、主色调、风格和视角。使用由多样化文本提示生成的14,580张图像数据集，对七种领先的多模态模型进行了评测，考察其在准确识别和描述各视觉特征方面的能力，揭示了各模型在全面图像理解上的优势与局限。研究结果对多模态模型在各类图像分析任务中的开发与选择具有重要指导意义。
原文摘要 · Abstract (English)
This article introduces a benchmark designed to evaluate the capabilities of multimodal models in analyzing and interpreting images. The benchmark focuses on seven key visual aspects: main object, additional objects, background, detail, dominant colors, style, and viewpoint. A dataset of 14,580 images, generated from diverse text prompts, was used to assess the performance of seven leading multimodal models. These models were evaluated on their ability to accurately identify and describe each visual aspect, providing insights into their strengths and weaknesses for comprehensive image understanding. The findings of this benchmark have significant implications for the development and selection of multimodal models for various image analysis tasks.
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