浏览全部资源
扫码关注微信
Published： 16 June 2023 ，
移动端阅览
陶钧， 张宇， 陈晴， 刘灿， 陈思明， 袁晓如. 2023. 智能可视化与可视分析. 中国图象图形学报， 28(06):1909-1926
Tao Jun， Zhang Yu， Chen Qing， Liu Can， Chen Siming， Yuan Xiaoru. 2023. Intelligent visualization and visual analytics. Journal of Image and Graphics， 28(06):1909-1926
可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解、分析数据的常见手段。人工智能可以通过对大数据的学习分析提高数据质量，捕捉关键信息，并选取最有效的视觉呈现方式，从而使用户更快、更准确、更全面地从可视化中理解数据。利用人工智能方法，交互式可视化系统也能更好地学习用户习惯及用户意图，推荐符合用户需求的可视化形式、交互操作和数据特征，从而降低用户探索的学习及时间成本，提高交互分析的效率。人工智能方法在可视化中的应用受到了极大关注，产生了大量学术成果。本文从最新工作出发，探讨人工智能在可视化流程的关键步骤中的作用。包括如何智能地表示和管理数据、如何辅助用户快速创建和定制可视化、如何通过人工智能扩展交互手段及提高交互效率、如何借助人工智能辅助数据的交互分析等。具体而言，本文详细梳理每个步骤中需要完成的任务及解决思路，介绍相应的人工智能方法（如深度网络结构），并以图表数据为例介绍智能可视化与可视分析的应用，最后讨论智能可视化方法的发展趋势，展望未来的研究方向及应用场景。
Visualization and visual analysis have become an key instrument in analyzing and understanding data in the era of big data. Visualization maps data to the visual channel through visual encoding， allowing users to quickly access multi-dimensional information from large amounts of data through the human perception， while visual analysis builds an interaction loop between data and users based on the visual representation， and promotes the user’s reasoning on complex data analysis through interactive visual interfaces. However， as the current data scale continues to grow and its structure becomes increasingly complex， the rich information exceeds the expression ability of the screen space and the processing ability of human visual perception. In this context， straightforward visual encoding cannot effectively convey all the information in data any longer. In addition， interactive exploration is challenged of large-scale complex data as well. It is unfeasible for users to determine the exploration direction based on experience or simple observation of data. Users may fall into time-consuming trial-and-error attempts without uncovering the insights hidden in the data. Therefore， it becomes natural to shift the effort from users to artificial intelligence. The most recent visualization and visual analytic approaches often rely on artificial intelligence （AI） methods to analyze， understand， and summarize data， extract key structures and relationships from data， simplify visualization content， optimize information transmission in visual performance forms， and provide guidance and direction for interactive exploration. With the advancement of AI methods （such as deep learning）， machine intelligence is constantly increasing its ability to fit， analyze， and reason about complex data and transformations. This empowers artificial intelligence to overcome the heterogeneity across multi-modal data， such as the data elements， user intention， and visual representation. It further enables AI to establish complex connections among these heterogeneous factors involved in visual exploration. Accordingly， leveraging the power of AI to enhance visualization and visual analysis systems has attracted significant research attention from the visualization community in the past several years. This research direction inspires a wide diversity of research works. Some might focus on improving the performance of traditional computation tasks in visualization with AI methods， while other works even expands the boundaries of visualization methods and explores new research opportunities. For example， in visualization creation， by learning to accurately match data features and user intentions， the AI-powered approaches can automatically create visualizations showing the key information of users’ interest. These approaches effectively reduce the requirements of professional visualization skills and relieve the burden on users in manual operations. For scientific visualization， by observing a large number of simulation members， the AI-enabled techniques can quickly generate renderings of different simulation parameters and visualization parameters in interaction without the need for time-consuming simulations or complex rendering. For interactive exploration， by incorporating various kinds of interaction means， machine learning-based methods can reduce the learning and usage cost for users to use interactive systems， and， therefore attract a broader range of users to experience visualization systems. For visual analysis， by learning user behaviors and analyzing data， the intelligent approaches can suggest interaction operations during exploration， reducing trial-and-error costs and improving analysis efficiency. The literature review briefly introduces the AI-driven visualization researches， summarizes these approaches by categories， and discuss their applications and development. The survey covers four key tasks in visualization： data-oriented visualization management， visualization creation， interactive exploration， and visual analysis. Data management focuses on how to represent and manage large-scale integrated data to support subsequent high-precision rendering. Visualization creation focuses on how to map data to informative visual representations. Interactive exploration discusses how to enrich the interaction means during visual explorations. Visual analysis emphasizes how to combine visualization and interaction to facilitate intuitive and efficient data analysis. These four key tasks cover the entire visualization process from data to visual presentation and then to the human cognition. The survey further discusses the application of intelligent visualization and visual analysis using chart data as an example. Finally， the paper will discuss the development trend of intelligent visualization and visual analysis and point out the potential research directions in future. In terms of artificial intelligence methods， this survey focuses on the application of the new-generation of intelligence methods， with deep learning being one of the most prominent representative， in the field of visualization. It does not elaborate on the use traditional learning methods， such as linear optimization and cluster analysis， etc. Intelligent visualization becomes central to the entire discipline of visualization with many potential applications. Several key trends can be observed in its development. For data representation， the trend is to move from structured data on regular grids towards a more flexible and effective space using deep representation， commonly through networks based on multi-layer perceptrons. The improved computation space can open up possibilities of further researches， such as 1） generalizing the neural network to handle multiple variables， 2） multiple simulations （ensembles）， and 3） multiple tasks. For intelligent creation of visualization， applications of AI include using natural language generation algorithms to enhance the connection between users and intelligent tools， using data-driven models to strengthen these tools， and making these tools more effective at recognizing and predicting the user’s design intent. For intelligent interaction， the research focus shifts to developing machine learning methods that enhance visualization expression and information transformation， moving away from rule-based processes with limited scalability and extensibility. For visual analysis， the trend moves towards using deep learning for content and interaction recommendation， interactive update to visual analysis models， and interpretation of user interactions. Overall， these trends demonstrate the potential for intelligent visualization to significantly improve the efficiency and accuracy of data analysis and communication.
可视化可视分析机器学习深度学习前沿报告
visualizationvisual analyticsmachine learningdeep learningfrontier report
Al-Zaidy R A and Giles C L. 2017. A machine learning approach for semantic structuring of scientific charts in scholarly documents//Proceedings of 2017 AAAI Conference on Innovative Applications. San Francisco， USA： AAAI Press： 4644-4649
Battle L， Duan P， Miranda Z， Mukusheva D， Chang R and Stonebraker M. 2018. Beagle： automated extraction and interpretation of visualizations from the web//Proceedings of 2018 SIGCHI Conference on Human Factors in Computing Systems. Montreal， Canada： ACM： 1-8 ［DOI： 10.1145/3173574.3174168http://dx.doi.org/10.1145/3173574.3174168］
Berger M， Li J and Levine J A. 2019. A generative model for volume rendering. IEEE Transactions on Visualization and Computer Graphics， 25（4）： 1636-1650 ［DOI： 10.1109/TVCG.2018.2816059http://dx.doi.org/10.1109/TVCG.2018.2816059］
Bostock M， Ogievetsky V and Heer J. 2011. D3： data-driven documents. IEEE Transactions on Visualization and Computer Graphics， 17（12）： 2301-2309 ［DOI： 10.1109/tvcg.2011.185http://dx.doi.org/10.1109/tvcg.2011.185］
Brosz J， Nacenta M A， Pusch R， Carpendale S and Hurter C. 2013. Transmogrification： causal manipulation of visualizations//St. Andrews， Scotland， UK： ACM： 97-106 ［DOI： 10.1145/2501988.2502046http://dx.doi.org/10.1145/2501988.2502046］
Buitelaar P， Wood I D， Negi S， Arcan M， McCrae J P， Abele A， Robin C， Andryushechkin V， Ziad H， Sagha H， Schmitt M， Schuller B W， Sánchez-Rada J F， Iglesias C A， Navarro C， Giefer A， Heise N， Masucci V， Danza F A， Caterino C， Smrž P， Hradiš M， Povolný F， Klimeš M， Matějka P and Tummarello G. 2018. MixedEmotions： an open-source toolbox for multimodal emotion analysis. IEEE Transactions on Multimedia， 20（9）： 2454-2465 ［DOI： 10.1109/TMM.2018.2798287http://dx.doi.org/10.1109/TMM.2018.2798287］
Carberry S， Elzer S and Demir S. 2006. Information graphics： an untapped resource for digital libraries//Proceedings of the 29th annual international ACM SIGR Conforence on Research and Development in Information Retrieval. Seattle， USA： ACM： 581-588 ［DOI： 10.1145/1148170.1148270http://dx.doi.org/10.1145/1148170.1148270］
Chen Q， Cao S， Wang J and Cao N. 2023. How does automation shape the process of narrative visualization： a survey on tools.IEEE Transactions on Visualization and Computer Graphics:#3261320 [DOI: 10.1109/TVCG.2023.3261320http://dx.doi.org/10.1109/TVCG.2023.3261320]
Chen Q， Sun F， Xu X， Chen Z， Wang J and Cao N. 2022a. VizLinter： a linter and fixer framework for data visualization. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 206-216 ［DOI： 10.1109/TVCG.2021.3114804http://dx.doi.org/10.1109/TVCG.2021.3114804］
Chen Z， Cafarella M and Adar E. 2015. DiagramFlyer： a search engine for data-driven diagrams//Proceedings of 2015 International Conference on World Wide Web. Florence， Italy： ACM： 183-186 ［DOI： 10.1145/2740908.2742831http://dx.doi.org/10.1145/2740908.2742831］
Chen Z， Wang Y， Wang Q， Wang Y and Qu H. 2020. Towards automated infographic design： deep learning-based auto-extraction of extensible timeline. IEEE Transactions on Visualization and Computer Graphics， 26（1）： 917-926 ［DOI： 10.1109/TVCG.2019.2934810http://dx.doi.org/10.1109/TVCG.2019.2934810］
Chen Z， Ye S， Chu X， Xia H， Zhang H， Qu H and Wu Y. 2022b. Augmenting sports videos with VisCommentator. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 824-834 ［DOI： 10.1109/TVCG.2021.3114806http://dx.doi.org/10.1109/TVCG.2021.3114806］
Cheng F， Liu D， Du F， Lin Y， Zytek A， Li H， Qu H and Veeramachaneni K. 2022. VBridge： connecting the dots between features and data to explain healthcare models. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 378-388 ［DOI： 10.1109/TVCG.2021.3114836http://dx.doi.org/10.1109/TVCG.2021.3114836］
Choi J， Jung S， Park D G， Choo J and Elmqvist N. 2019. Visualizing for the non-visual： enabling the visually impaired to use visualization. Computer Graphics Forum， 38（3）： 249-260 ［DOI： 10.1111/cgf.13686http://dx.doi.org/10.1111/cgf.13686］
Cui W， Wang J， Huang H， Wang Y， Lin C Y， Zhang H and Zhang D. 2022. A mixed-initiative approach to reusing infographic charts. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 173-183 ［DOI： 10.1109/TVCG.2021.3114856http://dx.doi.org/10.1109/TVCG.2021.3114856］
Cui W， Zhang X， Wang Y， Huang H， Chen B， Fang L， Zhang H， Lou J G and Zhang D. 2020. Text-to-Viz： automatic generation of infographics from proportion-related natural language statements. IEEE Transactions on Visualization and Computer Graphics， 26（1）： 906-916 ［DOI： 10.1109/TVCG.2019.2934785http://dx.doi.org/10.1109/TVCG.2019.2934785］
Eirich J， Bonart J， Jäckle D， Sedlmair M， Schmid U， Fischbach K， Schreck T and Bernard J. 2022. IRVINE： a design study on analyzing correlation patterns of electrical engines. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 11-21 ［DOI： 10.1109/TVCG.2021.3114797http://dx.doi.org/10.1109/TVCG.2021.3114797］
Fan A， Ma Y， Mancenido M and Maciejewski R. 2022. Annotating line charts for addressing deception//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New Orleans， USA： ACM： 80 ［DOI： 10.1145/3491102.3502138］
Fu J， Zhu B， Cui W， Ge S， Wang Y， Zhang H， Huang H， Tang Y， Zhang D and Ma X. 2021. Chartem： reviving chart images with data embedding. IEEE Transactions on Visualization and Computer Graphics， 27（2）： 337-346 ［DOI： 10.1109/TVCG.2020.3030351http://dx.doi.org/10.1109/TVCG.2020.3030351］
Fujiwara T， Wei X， Zhao J and Ma K. 2022. Interactive dimensionality reduction for comparative analysis. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 758-768 ［DOI： 10.1109/TVCG.2021.3114807http://dx.doi.org/10.1109/TVCG.2021.3114807］
Futrelle R P， Kakadiaris I A， Alexander J， Carriero C M， Nikolakis N and Futrelle J M. 1992. Understanding diagrams in technical documents. Computer， 25（7）： 75-78 ［DOI： 10.1109/2.144445http://dx.doi.org/10.1109/2.144445］
Gu P， Han J， Chen D Z and Wang C. 2021. Reconstructing unsteady flow data from representative streamlines via diffusion and deep-learning-based denoising. IEEE Computer Graphics and Applications， 41（6）： 111-121 ［DOI： 10.1109/MCG.2021.3089627http://dx.doi.org/10.1109/MCG.2021.3089627］
Guo L， Ye S， Han J， Zheng H， Gao H， Chen D Z， Wang J-X and Wang C. 2020. SSR-VFD： spatial super-resolution for vector field data analysis and visualization//2020 IEEE Pacific Visualization Symposium. Tianjin， China： IEEE： 71-80 ［DOI： 10.1109/PacificVis48177.2020.8737http://dx.doi.org/10.1109/PacificVis48177.2020.8737］
Han J， Tao J and Wang C. 2020. FlowNet： a deep learning framework for clustering and selection of streamlines and stream surfaces. IEEE Transactions on Visualization and Computer Graphics， 26（4）： 1732-1744 ［DOI： 10.1109/TVCG.2018.2880207http://dx.doi.org/10.1109/TVCG.2018.2880207］
Han J， Tao J， Zheng H， Guo H， Chen D Z and Wang C. 2019. Flow field reduction via reconstructing vector data from 3-D streamlines using deep learning. IEEE Computer Graphics and Applications， 39（4）： 54-67 ［DOI： 10.1109/MCG.2018.2881523http://dx.doi.org/10.1109/MCG.2018.2881523］
Han J， Zheng H， Chen D Z and Wang C. 2022. STNet： an end-to-end generative framework for synthesizing spatiotemporal super-resolution volumes. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 270-280 ［DOI： 10.1109/TVCG.2021.3114815http://dx.doi.org/10.1109/TVCG.2021.3114815］
Harper J and Agrawala M. 2014. Deconstructing and restyling D3 visualizations//Proceedings of 2014 ACM Symposium on User Interface Software and Technology. Honolulu， USA： ACM： 253-262 ［DOI： 10.1145/2642918.2647411http://dx.doi.org/10.1145/2642918.2647411］
He W， Wang J， Guo H， Shen H W and Peterka T. 2020a. CECAV-DNN： collective ensemble comparison and visualization using deep neural networks. Visual Informatics， 4（2）： 109-121 ［DOI： 10.1016/J.VISINF.2020.04.004http://dx.doi.org/10.1016/J.VISINF.2020.04.004］
He W， Wang J， Guo H， Wang K C， Shen H W， Raj M， Nashed Y S G and Peterka T. 2020b. InSituNet： deep image synthesis for parameter space exploration of ensemble simulations. IEEE Transactions on Visualization and Computer Graphics， 26（1）： 23-33 ［DOI： 10.1109/TVCG.2019.2934312http://dx.doi.org/10.1109/TVCG.2019.2934312］
Heer J and Bostock M. 2010. Crowdsourcing graphical perception： using mechanical Turk to assess visualization design//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Atlanta， USA： ACM： 203-212 ［DOI： 10.1145/1753326.1753357http://dx.doi.org/10.1145/1753326.1753357］
Hong F， Liu C and Yuan X. 2019. DNN-VolVis： interactive volume visualization supported by deep neural network//2019 IEEE Pacific Visualization Symposium. Bangkok， Thailand： IEEE： 282-291 ［DOI： 10.1109/PacificVis.2019.00041http://dx.doi.org/10.1109/PacificVis.2019.00041］
Hopkins A K， Correll M and Satyanarayan A. 2020. VisuaLint： sketchy in situ annotations of chart construction errors. Computer Graphics Forum， 39（3）： 219-228 ［DOI： 10.1111/cgf.13975http://dx.doi.org/10.1111/cgf.13975］
Horvitz E. 1999. Uncertainty， action， and interaction： in pursuit of mixed-initiative computing. IEEE Intelligent Systems： 17-20
Hu K， Bakker M A， Li S， Kraska T and Hidalgo C. 2019. VizML： a machine learning approach to visualization recommendation//Proceedings of 2019 CHI Conference on Human Factors in Computing Systems. Glasgow， UK： ACM： 128 ［DOI： 10.1145/3290605.3300358］
Huang J， Xi Y， Hu J and Tao J. 2023. FlowNL： asking the flow data in natural languages. IEEE Transactions on Visualization and Computer Graphics， 29（1）： 1200-1210 ［DOI： 10.1109/TVCG.2022.3209453http://dx.doi.org/10.1109/TVCG.2022.3209453］
Huang W and Tan C L. 2007. A system for understanding imaged infographics and its applications//2007 ACM Symposium on Document Engineering. Winnipeg， MB， Canada： ACM： 9-18 ［DOI： 10.1145/1284420.1284427http://dx.doi.org/10.1145/1284420.1284427］
Jadhav S， Deng G， Zawin M and Kaufman A E. 2022. COVID-view： diagnosis of COVID-19 using chest CT. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 227-237 ［DOI： 10.1109/TVCG.2021.3114851http://dx.doi.org/10.1109/TVCG.2021.3114851］
Jung D， Kim W， Song H， Hwang J， Lee B， Kim B and Seo J. 2017. ChartSense： interactive data extraction from chart images//Proceedings of 2017 SIGCHI Conference on Human Factors in Computing Systems. Denver， USA： ACM： 6706-6717 ［DOI： 10.1145/3025453.3025957http://dx.doi.org/10.1145/3025453.3025957］
Kassel J and Rohs M. 2018. Valletto： a multimodal interface for ubiquitous visual analytics//Extended Abstracts of 2018 CHI Conference on Human Factors in Computing Systems. Montreal Canada： Association for Computing Machinery： LBW005 ［DOI： 10.1145/3170427.3188445］
Ke M T， Fujimoto S and Imai T. 2013. SeeDB： a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nature Neuroscience， 16（8）： 1154-1161 ［DOI： 10.1038/nn.3447http://dx.doi.org/10.1038/nn.3447］
Kim Y， Lee B， Srinivasan A and Choe E K. 2021. Data@Hand： fostering visual exploration of personal data on smartphones leveraging speech and touch interaction//Proceedings of 2021 CHI Conference on Human Factors in Computing Systems. Yokohama， Japan： Association for Computing Machinery： #462 ［DOI： 10.1145/3411764.3445421http://dx.doi.org/10.1145/3411764.3445421］
Kong N and Agrawala M. 2012. Graphical overlays： using layered elements to aid chart reading. IEEE Transactions on Visualization and Computer Graphics， 18（12）： 2631-2638 ［DOI： 10.1109/TVCG.2012.229http://dx.doi.org/10.1109/TVCG.2012.229］
Kong N， Hearst M A and Agrawala M. 2014. Extracting references between text and charts via crowdsourcing//Proceedings of 2014 SIGCHI Conference on Human Factors in Computing Systems. Toronto， Canada： ACM： 31-40 ［DOI： 10.1145/2556288.2557241http://dx.doi.org/10.1145/2556288.2557241］
Kuhn G R， Oliveira M M and Fernandes L A F. 2008. An efficient naturalness-preserving image-recoloring method for dichromats. IEEE Transactions on Visualization and Computer Graphics， 14（6）： 1747-1754 ［DOI： 10.1109/TVCG.2008.112http://dx.doi.org/10.1109/TVCG.2008.112］
Lai C， Lin Z， Jiang R， Han Y， Liu C and Yuan X. 2020. Automatic annotation synchronizing with textual description for visualization//Proceedings of 2020 CHI Conference on Human Factors in Computing Systems. Honolulu， USA： Association for Computing Machinery： 1-13 ［DOI： 10.1145/3313831.3376443http://dx.doi.org/10.1145/3313831.3376443］
Latif S， Zhou Z， Kim Y， Beck F and Kim N W. 2022. Kori： interactive synthesis of text and charts in data documents. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 184-194 ［DOI： 10.1109/TVCG.2021.3114802http://dx.doi.org/10.1109/TVCG.2021.3114802］
Lee B， Srinivasan A， Isenberg P and Stasko J. 2021. Post-WIMP interaction for information visualization. Foundations and Trends in Human-Computer Interaction， 14（1）： 1-95 ［DOI： 10.1561/1100000081http://dx.doi.org/10.1561/1100000081］
Lee B， Srinivasan A， Stasko J， Tory M and Setlur V. 2018a. Multimodal interaction for data visualization//Proceedings of 2018 International Conference on Advanced Visual Interfaces. Castiglione della Pescaia， Italy： Association for Computing Machinery： #11 ［DOI： 10.1145/3206505.3206602http://dx.doi.org/10.1145/3206505.3206602］
Lee P， West J D and Howe B. 2018b. Viziometrics： analyzing visual information in the scientific literature. IEEE Transactions on Big Data， 4（1）： 117-129 ［DOI： 10.1109/TBDATA.2017.2689038http://dx.doi.org/10.1109/TBDATA.2017.2689038］
Li C， Baciu G， Wang Y， Chen J and Wang C. 2022. DDLVis： real-time visual query of spatiotemporal data distribution via density dictionary learning. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 1062-1072 ［DOI： 10.1109/TVCG.2021.3114762http://dx.doi.org/10.1109/TVCG.2021.3114762］
Lin J Q， Xie L J， Ji H F and Lu M J. 2022. StreamLNet： in-situ compression of flow field combined with streamline visualization. Journal of Computer-Aided Design and Computer Graphics， 34（7）： 1127-1137
林佳琦， 解利军， 季候风， 陆曼君. 2022. StreamLNet： 结合流线可视化的流场原位压缩方法. 计算机辅助设计与图形学学报， 34（7）： 1127-1137 ［DOI： 10.3724/SP.J.1089.2022.19110http://dx.doi.org/10.3724/SP.J.1089.2022.19110］
Liu C， Han Y， Jiang R and Yuan X. 2021. ADVIsor： automatic visualization answer for natural-language question on tabular data//Proceedings of 2021 IEEE the Pacific Visualization Symposium （PacificVis）. Tianjin， China： IEEE： 11-20 ［DOI： 10.1109/PacificVis52677.2021.00010http://dx.doi.org/10.1109/PacificVis52677.2021.00010］
Liu C， Xie L， Han Y， Wei D and Yuan X. 2020. AutoCaption： an approach to generate natural language description from visualization automatically//Proceedings of 2020 IEEE Pacific visualization symposium. Tianjin， China： IEEE： 191-195 ［DOI： 10.1109/PacificVis48177.2020.1043http://dx.doi.org/10.1109/PacificVis48177.2020.1043］
Liu L. 2022. A feature visualization method for time-varying volume data. Journal of Image and Graphics， 27（4）： 1302-1313
刘力. 2022. 面向时变体数据的特征可视化方法. 中国图象图形学报， 27（4）： 1302-1313 ［DOI： 10.11834/jig.200781http://dx.doi.org/10.11834/jig.200781］
Lu M， Liang J， Zhang Y， Li G， Chen S， Li Z and Yuan X. 2017. Interaction+： interaction enhancement for web-based visualizations//Proceedings of 2017 IEEE Pacific Visualization Symposium. Seoul， Korea （South）： IEEE： 61-70 ［DOI： 10.1109/PACIFICVIS.2017.8031580http://dx.doi.org/10.1109/PACIFICVIS.2017.8031580］
Lu Y， Jiang K， Levine J A and Berger M. 2021. Compressive neural representations of volumetric scalar fields. Computer Graphics Forum， 40（3）： 135-146 ［DOI： 10.1111/CGF.14295http://dx.doi.org/10.1111/CGF.14295］
Mackinlay J. 1986. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics， 5（2）： 110-141 ［DOI： 10.1145/22949.22950http://dx.doi.org/10.1145/22949.22950］
Maher K， Huang Z， Song J， Deng X， Lai Y， Ma C， Wang H， Liu Y and Wang H. 2022. E-ffective： a visual analytic system for exploring the emotion and effectiveness of inspirational speeches. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 508-517 ［DOI： 10.1109/TVCG.2021.3114789http://dx.doi.org/10.1109/TVCG.2021.3114789］
Méndez G G， Nacenta M A and Vandenheste S. 2016. iVoLVER： interactive visual language for visualization extraction and reconstruction//Proceedings of 2016 SIGCHI Conference on Human Factors in Computing Systems. San Jose， USA： ACM： 4073-4085 ［DOI： 10.1145/2858036.2858435http://dx.doi.org/10.1145/2858036.2858435］
Min R P， Li Y F， Huang Y， Yang J Y and Zhong B J. 2022. Visualized all-scale shape representation and recognition. Journal of Image and Graphics， 27（2）： 628-641
闵睿朋， 李一凡， 黄瑶， 杨剑宇， 钟宝江. 2022. 形状的全尺度可视化表示与识别. 中国图象图形学报， 27（2）： 628-641 ［DOI： 10.11834/jig.200693http://dx.doi.org/10.11834/jig.200693］
Nair R R， Sankaran N， Nwogu I and Govindaraju V. 2015. Automated analysis of line plots in documents//Proceedings of 2015 International Conference on Document Analysis and Recognition. Tunis， Tunisia： IEEE： 796-800 ［DOI： 10.1109/ICDAR.2015.7333871http://dx.doi.org/10.1109/ICDAR.2015.7333871］
Narechania A， Karduni A， Wesslen R and Wall E. 2022. VITALITY： promoting serendipitous discovery of academic literature with transformers and visual analytics. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 486-496 ［DOI： 10.1109/TVCG.2021.3114820http://dx.doi.org/10.1109/TVCG.2021.3114820］
Poco J and Heer J. 2017. Reverse-engineering visualizations： recovering visual encodings from chart images. Computer Graphics Forum， 36（3）： 353-363 ［DOI： 10.1111/cgf.13193http://dx.doi.org/10.1111/cgf.13193］
Poco J， Mayhua A and Heer J. 2018. Extracting and retargeting color mappings from bitmap images of visualizations. IEEE Transactions on Visualization and Computer Graphics， 24（1）： 637-646 ［DOI： 10.1109/TVCG.2017.2744320http://dx.doi.org/10.1109/TVCG.2017.2744320］
Qian C， Sun S， Cui W， Lou J G， Zhang H and Zhang D. 2021. Retrieve-then-adapt： example-based automatic generation for proportion-related infographics. IEEE Transactions on Visualization and Computer Graphics， 27（2）： 443-452 ［DOI： 10.1109/TVCG.2020.3030448http://dx.doi.org/10.1109/TVCG.2020.3030448］
Ren S， He K， Girshick R and Sun J. 2015. Faster R-CNN： towards real-time object detection with region proposal networks//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal， Canada： MIT Press： 91-99
Sahoo S， Lu Y and Berger M. 2022. Neural flow map reconstruction. Computer Graphics Forum， 41（3）： 391-402 ［DOI： 10.1111/CGF.14549http://dx.doi.org/10.1111/CGF.14549］
Savva M， Kong N， Chhajta A， Li F F， Agrawala M and Heer J. 2011. ReVision： automated classification， analysis and redesign of chart images//Proceedings of 2011 ACM Symposium on User Interface Software and Technology. Santa Barbara， USA： ACM： 393-402 ［DOI： 10.1145/2047196.2047247http://dx.doi.org/10.1145/2047196.2047247］
Seipel P， Stock A， Santhanam S， Baranowski A， Hochgeschwender N and Schreiber A. 2019. Speak to your software visualization-exploring component-based software architectures in augmented reality with a conversational interface//Proceedings of Working Conference on Software Visualization （VISSOFT）. Cleveland， USA： IEEE： 78-82 ［DOI： 10.1109/VISSOFT.2019.00017http://dx.doi.org/10.1109/VISSOFT.2019.00017］
Shi D， Sun F， Xu X， Lan X， Gotz D and Cao N. 2021a. AutoClips： an automatic approach to video generation from data facts. Computer Graphics Forum， 40（3）： 495-505 ［DOI： 10.1111/cgf.14324http://dx.doi.org/10.1111/cgf.14324］
Shi D， Xu X， Sun F， Shi Y and Cao N. 2021b. Calliope： automatic visual data story generation from a spreadsheet. IEEE Transactions on Visualization and Computer Graphics， 27（2）： 453-463 ［DOI： 10.1109/TVCG.2020.3030403http://dx.doi.org/10.1109/TVCG.2020.3030403］
Siegel N， Horvitz Z， Levin R， Divvala S and Farhadi A. 2016. FigureSeer： parsing result-figures in research papers//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 664-680 ［DOI： 10.1007/978-3-319-46478-7_41http://dx.doi.org/10.1007/978-3-319-46478-7_41］
Song H， Dai Z， Xu P and Ren L. 2022. Interactive visual pattern search on graph data via graph representation learning. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 335-345 ［DOI： 10.1109/TVCG.2021.3114857http://dx.doi.org/10.1109/TVCG.2021.3114857］
Srinivasan A， Lee B， Henry Riche N， Drucker S M and Hinckley K. 2020. InChorus： designing consistent multimodal interactions for data visualization on tablet devices//Proceedings of 2020 CHI Conference on Human Factors in Computing Systems. Honolulu， USA： Association for Computing Machinery： 1-13 ［DOI： 10.1145/3313831.3376782http://dx.doi.org/10.1145/3313831.3376782］
Sun J， Wu C， Ge Y， Li Y and Yu H. 2019. Spatial-temporal scientific data clustering via deep convolutional neural network//Proceedings of 2019 IEEE International Conference on Big Data. Los Angeles， USA： IEEE： 3424-3429 ［DOI： 10.1109/BigData47090.2019.9006507http://dx.doi.org/10.1109/BigData47090.2019.9006507］
Tang T， Wu Y， Wu Y， Yu L and Li Y. 2022. VideoModerator： a risk-aware framework for multimodal video moderation in e-commerce. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 846-856 ［DOI： 10.1109/TVCG.2021.3114781http://dx.doi.org/10.1109/TVCG.2021.3114781］
Tkachev G， Frey S and Ertl T. 2019. Local prediction models for spatiotemporal volume visualization. IEEE Transactions on Visualization and Computer Graphics， 27（7）： 3091-3108 ［DOI： 10.1109/TVCG.2019.2961893http://dx.doi.org/10.1109/TVCG.2019.2961893］
Voeikov R， Falaleev N and Baikulov R. 2020. TTNet： real-time temporal and spatial video analysis of table tennis//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle， USA： IEEE： 3866-3874 ［DOI： 10.1109/CVPRW50498.2020.00450http://dx.doi.org/10.1109/CVPRW50498.2020.00450］
Wang J， Yu L， Lai K and Zhang X. 2016. Dimensional sentiment analysis using a regional CNN-LSTM model//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin， Germany： ACL： 225-230 ［DOI： 10.18653/v1/P16-2037http://dx.doi.org/10.18653/v1/P16-2037］
Wang Y， Sun Z， Zhang H， Cui W， Xu K， Ma X and Zhang D. 2020. DataShot： automatic generation of fact sheets from tabular data. IEEE Transactions on Visualization and Computer Graphics， 26（1）： 895-905 ［DOI： 10.1109/TVCG.2019.2934398http://dx.doi.org/10.1109/TVCG.2019.2934398］
Wang Y， Gao Y， Huang R， Cui W， Zhang H and Zhang D. 2021. Animated presentation of static infographics with InfoMotion. Computer Graphics Forum， 40（3）： 507-518 ［DOI： 10.1111/cgf.14325http://dx.doi.org/10.1111/cgf.14325］
Wang Y， Zhang H， Huang H， Chen X， Yin Q， Hou Z， Zhang D， Luo Q and Qu H. 2018. InfoNice： easy creation of information graphics//Proceedings of 2018 SIGCHI Conference on Human Factors in Computing Systems. Montreal， Canada： ACM： #335 ［DOI： 10.1145/3173574.3173909http://dx.doi.org/10.1145/3173574.3173909］
Weiss J and Navab N. 2021. Deep direct volume rendering： learning visual feature mappings from exemplary images ［EB/OL］. ［2023-03-23］. https://arxiv.org/pdf2106.05429.pdfhttps://arxiv.org/pdf2106.05429.pdf
Weiss S， Hermüller P and Westermann R. 2022. Fast neural representations for direct volume rendering. Computer Graphics Forum， 41（6）： 196-211 ［DOI： 10.1111/CGF.14578http://dx.doi.org/10.1111/CGF.14578］
Wongsuphasawat K， Moritz D， Anand A， Mackinlay J， Howe B and Heer J. 2016. Towards a general-purpose query language for visualization recommendation//Proceedings of 2016 Workshop on Human-In-the-Loop Data Analytics. San Francisco， USA： ACM： #4 ［DOI： 10.1145/2939502.2939506http://dx.doi.org/10.1145/2939502.2939506］
Wongsuphasawat K， Moritz D， Anand A， Mackinlay J， Howe B and Heer J. 2016. Voyager： exploratory analysis via faceted browsing of visualization recommendations. IEEE Transactions on Visualization and Computer Graphics， 22（1）： 649-658 ［DOI： 10.1109/TVCG.2015.2467191http://dx.doi.org/10.1109/TVCG.2015.2467191］
Wongsuphasawat K， Qu Z， Moritz D， Chang R， Ouk F， Anand A， Mackinlay J， Howe B and Heer J. 2017. Voyager 2： augmenting visual analysis with partial view specifications//Proceedings of 2017 CHI Conference on Human Factors in Computing Systems. Denver， USA： ACM： 2648-2659 ［DOI： 10.1145/3025453.3025768http://dx.doi.org/10.1145/3025453.3025768］
Wu J， Liu D， Guo Z， Xu Q and Wu Y. 2022. TacticFlow： visual analytics of ever-changing tactics in racket sports. IEEE Transactions on Visualization and Computer Graphics， 28（1）： 835-845 ［DOI： 10.1109/TVCG.2021.3114832http://dx.doi.org/10.1109/TVCG.2021.3114832］
Xie Y， Franz E， Chu M and Thuerey N. 2018. TempoGAN： a temporally coherent， volumetric GAN for super-resolution fluid flow. ACM Transactions on Graphics， 37（4）： #95 ［DOI： 10.1145/3197517.3201304http://dx.doi.org/10.1145/3197517.3201304］
Xu J， Guo H， Shen H-W， Raj M， Wurster S W and Peteka T. 2022. Reinforcement learning for load-balanced parallel particle tracing. IEEE Transactions on Visualization and Computer Graphics ［10.1109/TVCG.2022.3148745http://dx.doi.org/10.1109/TVCG.2022.3148745］
Yu B and Silva C T. 2020. FlowSense： a natural language interface for visual data exploration within a dataflow system. IEEE Transactions on Visualization and Computer Graphics， 26（1）： 1-11 ［DOI： 10.1109/TVCG.2019.2934668http://dx.doi.org/10.1109/TVCG.2019.2934668］
Zhang P， Li C and Wang C. 2021a. VisCode： embedding information in visualization images using encoder-decoder network. IEEE Transactions on Visualization and Computer Graphics， 27（2）： 326-336 ［DOI： 10.1109/TVCG.2020.3030343http://dx.doi.org/10.1109/TVCG.2020.3030343］
Zhang Y， Coecke B and Chen M. 2021b. MI3： machine-initiated intelligent interaction for interactive classification and data reconstruction. ACM Transactions on Interactive Intelligent Systems， 11（3/4）： #18 ［DOI： 10.1145/3412848http://dx.doi.org/10.1145/3412848］
Zhao J， Fan M and Feng M. 2022. ChartSeer： interactive steering exploratory visual analysis with machine intelligence. IEEE Transactions on Visualization and Computer Graphics， 28（3）： 1500-1513 ［DOI： 10.1109/TVCG.2020.3018724http://dx.doi.org/10.1109/TVCG.2020.3018724］
Zhao J， Xu S， Chandrasegaran S， Bryan C， Du F， Mishra A， Qian X， Li Y and Ma K L. 2023. ChartStory： automated partitioning， layout， and captioning of charts into comic-style narratives. IEEE Transactions on Visualization and Computer Graphics， 29（2）： 1384-1399 ［DOI： 10.1109/TVCG.2021.3114211http://dx.doi.org/10.1109/TVCG.2021.3114211］
Zhao Z， Marr R and Elmqvist N. 2015. Data Comics： Sequential Art for Data-Driven Storytelling. HCIL Technical Report HCIL-2015-15. University of Maryland
Zhou Z， Hou Y， Wang Q， Chen G， Lu J， Tao Y and Lin H. 2017. Volume upscaling with convolutional neural networks//Proceedings of 2017 Computer Graphics International Conference. Yokohama， Japan： ACM： #38 ［DOI： 10.1145/3095140.3095178http://dx.doi.org/10.1145/3095140.3095178］
相关作者
相关机构
京公网安备11010802024621