发表机构
Zhejiang University; Florida State University; Hangzhou City University（浙江大学; 佛罗里达州立大学; 杭州城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对艺术品隐式语义标注效率低的问题，提出双向人机增强框架并实现ArtAnno系统，经评估可提升标注效率、实现知识积累并减少标注员的信息处理工作量。
AI 中文摘要
高质量的艺术品标注对于计算艺术研究至关重要，但由于图像背后依赖于文化语境意义和深度背景知识，提取隐式语义仍然具有挑战性。当前的AI辅助标注工具往往缺乏辅助，或依赖单向工作流，专家必须进行额外的手动校准来改进AI模型，导致效率有限。为解决这一问题，我们提出Bidirectional Human-AI Augmentation（BiHAA，双向人机增强），这是一个闭环框架，技能和领域知识库通过实时交互与双向人机增强不断演化。在对20名不同背景的艺术品标注员进行形成性研究的基础上，我们将该框架实现为ArtAnno，一个由多智能体架构驱动的艺术品标注系统。该系统包含主动智能体支持模块，其中AI通过语义挖掘和标签建议增强人类；以及交互驱动演化模块，其中人类专业知识通过将标注轨迹提炼为可复用经验，持续提升AI。通过用户研究和两个案例研究的评估表明，我们的框架和系统提升了标注效率，实现了知识积累，并减少了领域专业知识有限的标注员的信息检索与验证工作量。我们最后讨论了更广泛的影响和未来方向。
英文摘要
High-quality annotation of artworks is essential for computational art research, yet extracting implicit semantics remains challenging due to the reliance on culturally grounded meanings and deep contextual knowledge behind the images. Current AI-assisted annotation tools often lack assistance or rely on one-way workflows where experts have to perform extra manual calibrations to improve AI models, resulting in limited efficiency. To address this, we propose Bidirectional Human-AI Augmentation(BiHAA), a closed-loop framework in which skills and domain knowledge base evolve through real-time interaction and bidirectional HAI augmentation. Informed by a formative study with 20 artwork annotators from different backgrounds, we implement this framework in ArtAnno, an artwork annotation system driven by a multi-agent architecture. The system includes a Proactive Agentic Support Module, where AI augments humans through semantic mining and label suggestion, and an Interaction-Driven Evolution Module, where human expertise continuously enhances the AI through distilling annotation trajectories into reusable experience. Evaluation through a user study and two case studies demonstrates that our framework and system improve annotation efficiency, enable knowledge accumulation, and reduce the effort of information seeking and verification for annotators with limited domain expertise. We conclude by discussing broader implications and future directions.
