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240331s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2024.3383089
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Chen, Qing
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Chart2Vec
|b A Universal Embedding of Context-Aware Visualizations
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| 264 |
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|c 2024
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a Date Revised 29.03.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a 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
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Chen, Ying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zou, Ruishi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shuai, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Yi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Jiazhe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Nan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g PP(2024) vom: 29. März
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|x 1941-0506
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|g month:03
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|u http://dx.doi.org/10.1109/TVCG.2024.3383089
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