We propose AutoLegend to generate interactive visualization legends using online learning with user feedback. AutoLegend accurately extracts symbols and channels from visualizations and then generates quality legends. AutoLegend enables a two-way interaction between legends and interactions, including highlighting, filtering, data retrieval, and retargeting. After analyzing visualization legends from IEEE VIS papers over the past 20 years, we summarized the design space and evaluation metrics for legend design in visualizations, particularly charts. The generation process consists of three interrelated components: a legend search agent, a feedback model, and an adversarial loss model. The search agent determines suitable legend solutions by exploring the design space and receives guidance from the feedback model through scalar scores. The feedback model is continuously updated by the adversarial loss model based on user input. The user study revealed that AutoLegend can learn users' preferences through legend editing.

翻译：我们提出AutoLegend，一种利用在线学习结合用户反馈生成交互式可视化图例的方法。AutoLegend能够准确提取可视化中的符号与视觉通道，并据此生成高质量图例。该系统实现了图例与交互操作之间的双向互动，包括高亮、筛选、数据检索与重定向等功能。通过分析过去20年IEEE VIS会议论文中的可视化图例，我们总结了可视化（尤其是图表）中图例设计的设计空间与评估指标。生成过程包含三个相互关联的组件：图例搜索代理、反馈模型与对抗损失模型。搜索代理通过探索设计空间确定合适的图例方案，并依据反馈模型提供的标量评分获得指导。反馈模型则通过对抗损失模型基于用户输入进行持续更新。用户研究表明，AutoLegend能够通过图例编辑过程学习用户的偏好。