AutoLegend: A User Feedback-Driven Adaptive Legend Generator for Visualizations
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914882683666432 |
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| author | Liu, Can Mei, Xiyao Jiang, Zhibang Tan, Shaocong Yuan, Xiaoru |
| author_facet | Liu, Can Mei, Xiyao Jiang, Zhibang Tan, Shaocong Yuan, Xiaoru |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoLegend: A User Feedback-Driven Adaptive Legend Generator for Visualizations Liu, Can Mei, Xiyao Jiang, Zhibang Tan, Shaocong Yuan, Xiaoru Human-Computer Interaction 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. |
| title | AutoLegend: A User Feedback-Driven Adaptive Legend Generator for Visualizations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.16331 |