文本到图像生成模型的多模态基准测试与推荐
Multimodal Benchmarking and Recommendation of Text-to-Image Generation Models
AI总结：
本文提出一个开源统一评估框架，利用DeepFashion-MultiModal数据集和多种定量指标，证明元数据增强提示能提升文本到图像模型的视觉真实感、语义保真度与鲁棒性，并支持任务特定的模型选择与提示设计推荐。
摘要：
本文提出了一个开源的统一基准测试与评估框架，用于文本到图像生成模型，特别关注元数据增强提示的影响。利用DeepFashion-MultiModal数据集，我们通过一套全面的定量指标评估生成输出，包括Weighted Score、基于CLIP（对比语言-图像预训练）的相似度、LPIPS（学习感知图像块相似度）、FID（弗雷歇初始距离）和基于检索的度量，以及定性分析。我们的结果表明，结构化元数据增强显著提升了视觉真实感、语义保真度和模型在不同文本到图像架构上的鲁棒性。虽然不是一个传统的推荐系统，但我们的框架能够基于评估指标为模型选择和提示设计提供任务特定的推荐。
英文摘要：
This work presents an open-source unified benchmarking and evaluation framework for text-to-image generation models, with a particular focus on the impact of metadata augmented prompts. Leveraging the DeepFashion-MultiModal dataset, we assess generated outputs through a comprehensive set of quantitative metrics, including Weighted Score, CLIP (Contrastive Language Image Pre-training)-based similarity, LPIPS (Learned Perceptual Image Patch Similarity), FID (Frechet Inception Distance), and retrieval-based measures, as well as qualitative analysis. Our results demonstrate that structured metadata enrichments greatly enhance visual realism, semantic fidelity, and model robustness across diverse text-to-image architectures. While not a traditional recommender system, our framework enables task-specific recommendations for model selection and prompt design based on evaluation metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
