Chart2Vec: A Universal Embedding of Context-Aware Visualizations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916178530664448 |
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| author | Chen, Qing Chen, Ying Zou, Ruishi Shuai, Wei Guo, Yi Wang, Jiazhe Cao, Nan |
| author_facet | Chen, Qing Chen, Ying Zou, Ruishi Shuai, Wei Guo, Yi Wang, Jiazhe Cao, Nan |
| contents | The advances in AI-enabled techniques have accelerated the creation and automation of visualizations in the past decade. However, presenting visualizations in a descriptive and generative format remains a challenge. Moreover, current visualization embedding methods focus on standalone visualizations, neglecting the importance of contextual information for multi-view visualizations. To address this issue, we propose a new representation model, Chart2Vec, to learn a universal embedding of visualizations with context-aware information. Chart2Vec aims to support a wide range of downstream visualization tasks such as recommendation and storytelling. Our model considers both structural and semantic information of visualizations in declarative specifications. To enhance the context-aware capability, Chart2Vec employs multi-task learning on both supervised and unsupervised tasks concerning the cooccurrence of visualizations. We evaluate our method through an ablation study, a user study, and a quantitative comparison. The results verified the consistency of our embedding method with human cognition and showed its advantages over existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_08304 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Chart2Vec: A Universal Embedding of Context-Aware Visualizations Chen, Qing Chen, Ying Zou, Ruishi Shuai, Wei Guo, Yi Wang, Jiazhe Cao, Nan Human-Computer Interaction The advances in AI-enabled techniques have accelerated the creation and automation of visualizations in the past decade. However, presenting visualizations in a descriptive and generative format remains a challenge. Moreover, current visualization embedding methods focus on standalone visualizations, neglecting the importance of contextual information for multi-view visualizations. To address this issue, we propose a new representation model, Chart2Vec, to learn a universal embedding of visualizations with context-aware information. Chart2Vec aims to support a wide range of downstream visualization tasks such as recommendation and storytelling. Our model considers both structural and semantic information of visualizations in declarative specifications. To enhance the context-aware capability, Chart2Vec employs multi-task learning on both supervised and unsupervised tasks concerning the cooccurrence of visualizations. We evaluate our method through an ablation study, a user study, and a quantitative comparison. The results verified the consistency of our embedding method with human cognition and showed its advantages over existing methods. |
| title | Chart2Vec: A Universal Embedding of Context-Aware Visualizations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2306.08304 |