DiffusionWorldViewer：揭示并拓宽生成式文本到图像模型所反映的世界观
DiffusionWorldViewer: Exposing and Broadening the Worldview Reflected by Generative Text-to-Image Models
AI总结：
本文提出DiffusionWorldViewer交互界面，揭示并拓宽文本到图像模型的世界观，通过用户研究证明其能帮助用户表达多样观点并挑战模型局限。
摘要：
生成式文本到图像（TTI）模型能够根据简短文本描述生成高质量图像，并广泛应用于学术和创意领域。与人类一样，TTI模型具有世界观，即从其训练数据和任务中习得的世界概念，这会影响它们针对给定提示生成的图像。然而，TTI模型的世界观往往对用户隐藏，使用户难以建立对TTI输出的直觉理解，且这些世界观常与用户的世界观不一致，导致输出图像不符合用户预期。为此，我们提出了DiffusionWorldViewer，一个交互式界面，它揭示TTI模型在输出人口统计特征方面的世界观，并提供编辑工具，以将输出图像与用户视角对齐。在一项涉及18位多样化TTI用户的用户研究中，我们发现DiffusionWorldViewer帮助用户在生成的图像中表达其多样化的观点，并挑战当前TTI模型所反映的有限世界观。
英文摘要：
Generative text-to-image (TTI) models produce high-quality images from short textual descriptions and are widely used in academic and creative domains. Like humans, TTI models have a worldview, a conception of the world learned from their training data and task that influences the images they generate for a given prompt. However, the worldviews of TTI models are often hidden from users, making it challenging for users to build intuition about TTI outputs, and they are often misaligned with users' worldviews, resulting in output images that do not match user expectations. In response, we introduce DiffusionWorldViewer, an interactive interface that exposes a TTI model's worldview across output demographics and provides editing tools for aligning output images with user perspectives. In a user study with 18 diverse TTI users, we find that DiffusionWorldViewer helps users represent their varied viewpoints in generated images and challenge the limited worldview reflected in current TTI models.
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。
