Social Reward：基于在线创意社区百万用户反馈的生成式AI评估与增强
Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
AI总结：
本文提出Social Reward框架，利用Picsart平台百万用户隐式反馈构建数据集，训练预测模型评估文生图社区偏好，并据此微调模型生成更受欢迎的图像。
摘要：
作为社区认可的一种形式，社交奖励为在线平台用户参与互动和贡献内容提供了强大的动力。文本条件图像合成的最新进展开启了一个协作时代，AI赋能用户创作原创视觉艺术作品以寻求社区认可。然而，在集体社区偏好的背景下评估这些模型带来了独特的挑战。现有的评估方法主要集中在以图像质量和提示对齐为指导的有限规模用户研究上。这项工作开创了范式转变，推出了Social Reward——一个创新的奖励建模框架，利用参与生成图像创意编辑的社交网络用户的隐式反馈。我们进行了广泛的数据集整理和优化工作，数据来源于Picsart：一个在线视觉创作和编辑平台，由此产生了首个百万用户规模的隐式人类偏好数据集，名为Picsart Image-Social，专门针对用户生成的视觉艺术。我们的分析揭示了当前指标在建模文生图模型输出的社区创意偏好方面的不足，促使我们引入一个专门为解决这些限制而量身定制的新型预测模型。严格的定量实验和用户研究表明，我们的Social Reward模型比现有指标更符合社交受欢迎程度。此外，我们利用Social Reward微调文生图模型，生成的图像不仅更受Social Reward青睐，也更受其他既有指标青睐。这些发现突显了Social Reward在评估社区对AI生成艺术作品欣赏程度方面的相关性和有效性，与用户的创意目标建立了更紧密的一致性：创作受欢迎的视觉艺术。代码可在https://github.com/Picsart-AI-Research/Social-Reward获取。
英文摘要：
Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. The recent progress of text-conditioned image synthesis has ushered in a collaborative era where AI empowers users to craft original visual artworks seeking community validation. Nevertheless, assessing these models in the context of collective community preference introduces distinct challenges. Existing evaluation methods predominantly center on limited size user studies guided by image quality and prompt alignment. This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework that leverages implicit feedback from social network users engaged in creative editing of generated images. We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform, yielding a first million-user-scale dataset of implicit human preferences for user-generated visual art named Picsart Image-Social. Our analysis exposes the shortcomings of current metrics in modeling community creative preference of text-to-image models' outputs, compelling us to introduce a novel predictive model explicitly tailored to address these limitations. Rigorous quantitative experiments and user study show that our Social Reward model aligns better with social popularity than existing metrics. Furthermore, we utilize Social Reward to fine-tune text-to-image models, yielding images that are more favored by not only Social Reward, but also other established metrics. These findings highlight the relevance and effectiveness of Social Reward in assessing community appreciation for AI-generated artworks, establishing a closer alignment with users' creative goals: creating popular visual art. Codes can be accessed at https://github.com/Picsart-AI-Research/Social-Reward
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。
