AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (3.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review Article | Open Access

Foundation models meet visualizations: Challenges andopportunities

School of Software, Tsinghua University, Beijing 100084, China
Microsoft, Redmond 98052, USA
Show Author Information

Graphical Abstract

Abstract

Recent studies have indicated that foun-dation models, such as BERT and GPT, excel at adapting to various downstream tasks. This adap-tability has made them a dominant force in building artificial intelligence (AI) systems. Moreover, a new research paradigm has emerged as visualization techniques are incorporated into these models. This study divides these intersections into two research areas: visualization for foundation model (VIS4FM) and foundation model for visualization (FM4VIS). In terms of VIS4FM, we explore the primary role of visualizations in understanding, refining, and eva-luating these intricate foundation models. VIS4FM addresses the pressing need for transparency, explai-nability, fairness, and robustness. Conversely, in terms of FM4VIS, we highlight how foundation models can be used to advance the visualization field itself. The intersection of foundation models with visualizations is promising but also introduces a set of challenges. By highlighting these challenges and promising oppor-tunities, this study aims to provide a starting point for the continued exploration of this research avenue.

Keywords

visualizationartificial intelligence (AI)machine learningfoundation modelsvisualization for foundation model (VIS4FM)foundation model for visualization (FM4VIS)

References

[1]
Bommasani, R.; Hudson, D. A.; Adeli, E.; Altman, R.; Arora, S.; von Arx, S.; Bernstein, M. S.; Bohg, J.; Bosselut, A.; Brunskill, E.; et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.
Google Scholar
[2]
Devlin, J.; Chang, M. W.; Lee, K.; Toutanova. K. BERT: Pretraining of deep bidirectional transformers for language understanding. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186, 2019.
[3]
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations, 2021.
[4]
Wang, W.; Dai, J.; Chen, Z.; Huang, Z.; Li, Z.; Zhu, X.; Hu, X.; Lu, T.; Lu, L.; Li, H.; et al. InternImage: Exploring large-scale vision foundation models with deformable convolutions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14408-14419, 2023.
Crossref
[5]
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, 8748-8763, 2021.
[6]
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. Language models are few-shot learners. In: Proceedings of the 34th Conference on Neural Information Processing Systems, 1877-1901, 2020.
[7]
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. Training language models to follow instructions with human feedback. In: Proceedings of the 36th Conference on Neural Information Processing Systems, 27730-27744, 2022.
[8]
OpenAI; Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S. GPT-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
Google Scholar
[9]
Eloundou, T.; Manning, S.; Mishkin, P.; Rock, D. GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130, 2023.
Google Scholar
[10]
Liu, S.; Wang, X.; Liu, M.; Zhu, J. Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics Vol. 1, No. 1, 48-56, 2017.
Crossref Google Scholar
[11]
Choo, J.; Liu, S. Visual analytics for explainable deep learning. IEEE Computer Graphics and Applications Vol. 38, No. 4, 84-92, 2018.
Crossref Google Scholar
[12]
Hohman, F.; Kahng, M.; Pienta, R.; Chau, D. H. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 8, 2674-2693, 2019.
Crossref Google Scholar
[13]
Yuan, J.; Chen, C.; Yang, W.; Liu, M.; Xia, J.; Liu, S. A survey of visual analytics techniques for machine learning. Computational Visual Media Vol. 7, No. 1, 3-36, 2021.
Crossref Google Scholar
[14]
Sacha, D.; Kraus, M.; Keim, D. A.; Chen, M. VIS4ML: An ontology for visual analytics assisted machine learning. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 385-395, 2019.
Crossref Google Scholar
[15]
Wang, Q.; Chen, Z. T.; Wang, Y.; Qu, H. A survey on ML4VIS: Applying machine learning advances to data visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 12, 5134-5153, 2022.
Crossref Google Scholar
[16]
Wu, A.; Wang, Y.; Shu, X.; Moritz, D.; Cui, W.; Zhang, H.; Zhang, D.; Qu, H. AI4VIS: Survey on artificial intelligence approaches for data visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 12, 5049-5070, 2022.
Crossref Google Scholar
[17]
Wang, J.; Liu, S.; Zhang, W. Visual analytics for machine learning: A data perspective survey. arXiv preprint arXiv:2307.07712, 2023.
Crossref Google Scholar
[18]
Shen, L.; Shen, E.; Luo, Y.; Yang, X.; Hu, X.; Zhang, X.; Tai, Z.; Wang, J. Towards natural language interfaces for data visualization: A survey. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 6, 3121-3144, 2023.
Crossref Google Scholar
[19]
Liu, S.; Wang, X.; Collins, C.; Dou, W.; Ouyang, F.; El-Assady, M.; Jiang, L.; Keim, D. A. Bridging text visualization and mining: A task-driven survey. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 7, 2482-2504, 2019.
Crossref Google Scholar
[20]
Reif, E.; Kahng, M.; Petridis, S. Visualizing linguistic diversity of text datasets synthesized by large language models. arXiv preprint arXiv:2305.11364, 2023.
Crossref Google Scholar
[21]
Jin, Z.; Wang, X.; Cheng, F.; Sun, C.; Liu, Q.; Qu, H. ShortcutLens: A visual analytics approach for exploring shortcuts in natural language understanding dataset. IEEE Transactions on Visualization and Computer Graphics , 2023.
Crossref Google Scholar
[22]
Chen, C.; Yuan, J.; Lu, Y.; Liu, Y.; Su, H.; Yuan, S.; Liu, S. OoDAnalyzer: Interactive analysis of out-of-distribution samples. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 7, 3335-3349, 2021.
Crossref Google Scholar
[23]
Yang, W.; Li, Z.; Liu, M.; Lu, Y.; Cao, K.; Maciejewski, R.; Liu, S. Diagnosing concept drift with visual analytics. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 12-23, 2020.
Crossref
[24]
Liu, S.; Chen, C.; Lu, Y.; Ouyang, F.; Wang, B. An interactive method to improve crowdsourced annotations. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 235-245, 2019.
Crossref Google Scholar
[25]
Xiang, S.; Ye, X.; Xia, J.; Wu, J.; Chen, Y.; Liu, S. Interactive correction of mislabeled training data. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 57-68, 2019.
Crossref
[26]
Bäuerle, A.; Neumann, H.; Ropinski, T. Classifier-guided visual correction of noisy labels for image classification tasks. Computer Graphics Forum Vol. 39, No. 3, 195-205, 2020.
Crossref Google Scholar
[27]
Li, R.; Xiao, W.; Wang, L.; Jang, H.; Carenini, G. T3-Vis: Visual analytic for Training and fine-Tuning Transformers in NLP. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 220-230, 2021.
Crossref
[28]
DeRose, J. F.; Wang, J.; Berger, M. Attention flows: Analyzing and comparing attention mechanisms in language models. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1160-1170, 2021.
Crossref Google Scholar
[29]
Li, Y.; Wang, J.; Dai, X.; Wang, L.; Yeh, C. C M.; Zheng, Y.; Zhang, W.; Ma, K. L. How does attention work in vision transformers? A visual analytics attempt. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 6, 2888-2900, 2023.
Crossref Google Scholar
[30]
Yeh, C.; Chen, Y.; Wu, A.; Chen, C.; Viégas, F.; Wattenberg, M. AttentionViz: A global view of transformer attention. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 262-272, 2024.
Crossref Google Scholar
[31]
Li, Z.; Wang, X.; Yang, W.; Wu, J.; Zhang, Z.; Liu, Z.; Sun, M.; Zhang, H.; Liu, S. A unified understanding of deep NLP models for text classification. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 12, 4980-4994, 2022.
Crossref Google Scholar
[32]
Zhang, X.; Ono, J. P.; Song, H.; Gou, L.; Ma, K. L.; Ren, L. SliceTeller: A data slice-driven approach for machine learning model validation. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 842-852, 2023.
Crossref Google Scholar
[33]
Wei, Y.; Wang, Z.; Wang, Z.; Dai, Y.; Ou, G.; Gao, H.; Yang, H.; Wang, Y.; Cao, C. C.; Weng, L.; et al. Visual diagnostics of parallel performance in training large-scale DNN models. IEEE Transactions on Visualization and Computer Graphics , 2023.
Crossref Google Scholar
[34]
Wang, X.; Huang, R.; Jin, Z.; Fang, T.; Qu, H. CommonsenseVIS: Visualizing and understanding commonsense reasoning capabilities of natural language models. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 273-283, 2024.
Crossref Google Scholar
[35]
Sevastjanova, R.; Cakmak, E.; Ravfogel, S.; Cotterell, R.; El-Assady, M. Visual comparison of language model adaptation. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 1178-1188, 2023.
Crossref Google Scholar
[36]
Strobelt, H.; Webson, A.; Sanh, V.; Hoover, B.; Beyer, J.; Pfister, H.; Rush, A. M. Interactive and visual prompt engineering for ad-hoc task adaptation with large language models. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 1146-1156, 2023.
Crossref Google Scholar
[37]
Wu, S.; Shen, H.; Weld, D. S.; Heer, J.; Ribeiro, M. T. ScatterShot: Interactive In-context example curation for text transformation. In: Proceedings of the Proceedings of the 28th International Conference on Intelligent User Interfaces, 353-367, 2023.
Crossref
[38]
Feng, Y.; Wang, X.; Wong, K. K.; Wang, S.; Lu, Y.; Zhu, M.; Wang, B.; Chen, W. PromptMagician: Interactive prompt engineering for text-to-image creation. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 295-305, 2024.
Crossref Google Scholar
[39]
Wu, T.; Jiang, E.; Donsbach, A.; Gray, J.; Molina, A.; Terry, M.; Cai, C. J. PromptChainer: Chaining large language model prompts through visual programming. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 359, 2022.
Crossref
[40]
Wu, T.; Terry, M.; Cai, C. J. AI chains: Transparent and controllable human-AI interaction by chaining large language model prompts. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 385, 2022.
Crossref
[41]
Chung, J. J. Y.; Kim, W.; Yoo, K. M.; Lee, H.; Adar, E.; Chang, M. TaleBrush: Sketching stories with generative pretrained language models. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 209, 2022.
Crossref
[42]
Alsallakh, B.; Hanbury, A.; Hauser, H.; Miksch, S.; Rauber, A. Visual methods for analyzing probabilistic classification data. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1703-1712, 2014.
Crossref Google Scholar
[43]
Ren, D.; Amershi, S.; Lee, B.; Suh, J.; Williams, J. D. Squares: Supporting interactive performance analysis for multiclass classifiers. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 61-70, 2017.
Crossref Google Scholar
[44]
Görtler, J.; Hohman, F.; Moritz, D.; Wongsuphasawat, K.; Ren, D.; Nair, R.; Kirchner, M.; Patel, K. Neo: Generalizing confusion matrix visualization to hierarchical and multi-output labels. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 408, 2022.
Crossref
[45]
Chen, C.; Guo, Y.; Tian, F.; Liu, S.; Yang, W.; Wang, Z.; Wu, J.; Su, H.; Pfister, H.; Liu, S. A unified interactive model evaluation for classification, object detection, and instance segmentation in computer vision. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 76-86, 2024.
Crossref Google Scholar
[46]
Liu, S.; Andrienko, G.; Wu, Y.; Cao, N.; Jiang, L.; Shi, C.; Wang, Y. S.; Hong, S. Steering data quality with visual analytics: The complexity challenge. Visual Informatics Vol. 2, No. 4, 191-197, 2018.
Crossref Google Scholar
[47]
Jiang, L.; Liu, S.; Chen, C. Recent research advances on interactive machine learning. Journal of Visualization Vol. 22, No. 2, 401-417, 2019.
Crossref Google Scholar
[48]
Chen, C.; Wang, Z.; Wu, J.; Wang, X.; Guo, L. Z.; Li, Y. F.; Liu, S. Interactive graph construction for graph-based semi-supervised learning. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 9, 3701-3716, 2021.
Crossref Google Scholar
[49]
Chen, C.; Wu, J.; Wang, X.; Xiang, S.; Zhang, S. H.; Tang, Q.; Liu, S. Towards better captionsupervision for object detection. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 4, 1941-1954, 2022.
Crossref Google Scholar
[50]
Liu, M.; Shi, J.; Li, Z.; Li, C.; Zhu, J.; Liu, S. Towards better analysis of deep convolutional neural networks. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 91-100, 2017.
Crossref Google Scholar
[51]
Liu, M.; Shi, J.; Cao, K.; Zhu, J.; Liu, S. Analyzing the training processes of deep generative models. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 77-87, 2018.
Crossref Google Scholar
[52]
Sun, M.; Cai, L.; Cui, W.; Wu, Y.; Shi, Y.; Cao, N. Erato: Cooperative data story editing via fact interpolation. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 983-993, 2023.
Crossref Google Scholar
[53]
Ying, L.; Shu, X.; Deng, D.; Yang, Y.; Tang, T.; Yu, L.; Wu, Y. MetaGlyph: Automatic generation of metaphoric glyph-based visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 331-341, 2023.
Crossref Google Scholar
[54]
Guo, Y.; Han, Q.; Lou, Y.; Wang, Y.; Liu, C.; Yuan, X. Edit-history vis: An interactive visual exploration and analysis on wikipedia edit history. In: Proceedings of the IEEE 16th Pacific Visualization Symposium, 157-166, 2023.
Crossref
[55]
Tu, Y.; Qiu, R.; Wang, Y. S.; Yen, P. Y.; Shen, H. W. PhraseMap: Attention-based keyphrases recommendation for information seeking. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 3, 1787-1802, 2024.
Crossref Google Scholar
[56]
Li, X.; Wang, Y.; Wang, H.; Wang, Y.; Zhao, J. NBSearch: Semantic search and visual exploration of computational notebooks. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 308, 2021.
Crossref
[57]
Narechania, A.; Karduni, A.; Wesslen, R.; Wall, E. VITALITY: Promoting serendipitous discovery of academic literature with transformers & visual analytics. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 486-496, 2022.
Crossref Google Scholar
[58]
Shi, C.; Nie, F.; Hu, Y.; Xu, Y.; Chen, L.; Ma, X.; Luo, Q. MedChemLens: An interactive visual tool to support direction selection in interdisciplinary experimental research of medicinal chemistry. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 63-73, 2023.
Crossref Google Scholar
[59]
Resck, L. E.; Ponciano, J. R.; Nonato, L. G.; Poco, J. LegalVis: Exploring and inferring precedent citations in legal documents. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 6, 3105-3120, 2023.
Crossref Google Scholar
[60]
Zhang, X.; Engel, J.; Evensen, S.; Li, Y.; Demiralp, Ç.; Tan, W. C. Teddy: A system for interactive review analysis. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 108, 2020.
Crossref
[61]
Wu, Y.; Xu, Y.; Gao, S.; Wang, X.; Song, W.; Nie, Z.; Fan, X.; Li, Q. LiveRetro: Visual analytics for strategic retrospect in livestream E-commerce. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 1117-1127, 2024.
Crossref Google Scholar
[62]
Ouyang, Y.; Wu, Y.; Wang, H.; Zhang, C.; Cheng, F.; Jiang, C.; Jin, L.; Cao, Y.; Li, Q. Leveraging historical medical records as a proxy via multimodal modeling and visualization to enrich medical diagnostic learning. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 1238-1248, 2024.
Crossref Google Scholar
[63]
Tu, Y.; Li, O.; Wang, J.; Shen, H. W.; Powałko, P.; Tomescu-Dubrow, I.; Slomczynski, K. M.; Blanas, S.; Jenkins, J. C. SDRQuerier: A visual querying framework for cross-national survey data recycling. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 6, 2862-2874, 2023.
Crossref Google Scholar
[64]
Chen, Z.; Yang, Q.; Shan, J.; Lin, T.; Beyer, J.; Xia, H.; Pfister, H. IBall: Augmenting basketball videos with gaze-moderated embedded visualizations. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 841, 2023.
Crossref
[65]
Chen, Z. T.; Yang, Q.; Xie, X.; Beyer, J.; Xia, H.; Wu, Y.; Pfister, H. Sporthesia: Augmenting sports videos using natural language. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 918-928, 2023.
Crossref Google Scholar
[66]
Tu, Y.; Xu, J.; Shen, H. W. KeywordMap: Attention-based visual exploration for keyword analysis. In: Proceedings of the IEEE 14th Pacific Visualization Symposium, 206-215, 2021.
Crossref
[67]
Liu, C.; Han, Y.; Jiang, R.; Yuan, X. ADVISor: Automatic visualization answer for natural-language question on tabular data. In: Proceedings of the IEEE 14th Pacific Visualization Symposium, 11-20, 2021.
Crossref
[68]
Shen, L.; Zhang, Y.; Zhang, H.; Wang, Y. Data player: Automatic generation of data videos with narration-animation interplay. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 109-119, 2024.
Crossref Google Scholar
[69]
Xiao, S.; Huang, S.; Lin, Y.; Ye, Y.; Zeng, W. Let the chart spark: Embedding semantic context into chart with text-to-image generative model. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 284-294, 2024.
Crossref Google Scholar
[70]
Singh, H.; Shekhar, S. STL-CQA: Structure-based transformers with localization and encoding for chart question answering. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 3275-3284, 2020.
Crossref
[71]
Ma, W.; Zhang, H.; Yan, S.; Yao, G.; Huang, Y.; Li, H.; Wu, Y.; Jin, L. Towards an efficient framework for data extraction from chart images. In: Document Analysis and Recognition - ICDAR 2021. Lecture Notes in Computer Science, Vol. 12821. Lladós, J.; Lopresti, D.; Uchida, S. Eds. Springer Cham, 583-597, 2021.
Crossref
[72]
Song, S.; Li, C.; Sun, Y.; Wang, C. VividGraph: Learning to extract and redesign network graphs from visualization images. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 7, 3169-3181, 2023.
Crossref Google Scholar
[73]
Chen, Z. T.; Wang, Y.; Wang, Q.; Wang, Y.; Qu, H. Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 917-926, 2020.
Crossref Google Scholar
[74]
Sultanum, N.; Srinivasan, A. DATATALES: Investigating the use of large language models for authoring data-driven articles. In: Proceedings of the IEEE Visualization and Visual Analytics, 231-235, 2023.
Crossref
[75]
Liu, C.; Guo, Y.; Yuan, X. AutoTitle: An inter-active title generator for visualizations. IEEE Transactions on Visualization and Computer Graphics , 2023.
Crossref Google Scholar
[76]
Song, S.; Chen, J.; Li, C.; Wang, C. GVQA: Learning to answer questions about graphs with visualizations via knowledge base. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 464, 2023.
Crossref
[77]
Adhikary, J.; Vertanen, K. Text entry in virtual environments using speech and a midair keyboard. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 5, 2648-2658, 2021.
Crossref Google Scholar
[78]
Card, S. K.; Mackinlay, J. D.; Shneiderman, B. Readings in Information Visualization: Using Vision to Think. San Francisco, CA, USA: Academic Press, 1999.
[79]
Zhou, C.; Li, Q.; Li, C.; Yu, J.; Liu, Y.; Wang, G.; Zhang, K.; Ji, C.; Yan, Q.; He, L.; et al. A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT. arXiv preprint arXiv:2302.09419, 2023.
Google Scholar
[80]
Chen, Z. T.; Zeng, W.; Yang, Z.; Yu, L.; Fu, C. W.; Qu, H. LassoNet: Deep lasso-selection of 3D point clouds. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 195-204, 2020.
Crossref Google Scholar
[81]
Ottley, A.; Garnett, R.; Wan, R. Follow the clicks: Learning and anticipating mouse interactions during exploratory data analysis. Computer Graphics Forum Vol. 38, No. 3, 41-52, 2019.
Crossref Google Scholar
[82]
Brown, E. T.; Ottley, A.; Zhao, H.; Lin, Q.; Souvenir, R.; Endert, A.; Chang, R. Finding Waldo: Learning about users from their interactions. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1663-1672, 2014.
Crossref Google Scholar
[83]
Wexler, J.; Pushkarna, M.; Bolukbasi, T.; Wattenberg, M.; Viégas, F.; Wilson, J. The what-if tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 56-65, 2020.
Crossref Google Scholar
[84]
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; Gelly, S. Parameterefficient transfer learning for NLP. In: Proceedings of the 36th International Conference on Machine Learning, 2790-2799, 2019.
[85]
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W. LoRA: Low-rank adaptation of large language models. In: Proceedings of the International Conference on Learning Representations, 2021.
[86]
AdapterHub. Available at https://adapterhub.ml/
[87]
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Ichter, B.; Xia, F.; Chi, E.; Le, Q.; Zhou, D. Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of the 36th Conference on Neural Information Processing Systems, 24824-24837, 2022.
[88]
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Liu, P. J. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research Vol. 21, No. 1, 5485-5551, 2020.
Google Scholar
[89]
Wang, Y.; Hou, Z.; Shen, L.; Wu, T.; Wang, J.; Huang, H.; Zhang, H.; Zhang, D. Towards natural language-based visualization authoring. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 1222-1232, 2023.
Crossref Google Scholar
[90]
Schwartz, R.; Dodge, J.; Smith, N. A.; Etzioni, O. Green AI. Communications of the ACM Vol. 63, No. 12, 54-63, 2020.
Crossref Google Scholar
[91]
Zhou, C.; Liu, P.; Xu, P.; Lyer, S.; Sun, J.; Mao, Y.; Ma, X.; Efrat, A.; Yu, P.; Yu, L.; et al. LIMA: Less is more for alignment. In: Proceedings of the 37th Conference on Neural Information Processing Systems, 2024.
[92]
Zhou, Y.; Yang, W.; Chen, J.; Chen, C.; Shen, Z.; Luo, X.; Yu, L.; Liu, S. Cluster-aware grid layout. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 240-250, 2024.
Crossref Google Scholar
[93]
Yang, W.; Wang, X.; Lu, J.; Dou, W.; Liu, S. Interactive steering of hierarchical clustering. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 10, 3953-3967, 2021.
Crossref Google Scholar
[94]
Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, S. G.; Davis, A.; Dean, J.; Devin, M.; et al. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.
Google Scholar
[95]
Ma, K. L. In situ visualization at extreme scale: Challenges and opportunities. IEEE Computer Graphics and Applications Vol. 29, No. 6, 14-19, 2009.
Crossref Google Scholar
[96]
Rapp, T.; Peters, C.; Dachsbacher, C. Image-based visualization of large volumetric data using moments. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 6, 2314-2325, 2022.
Crossref Google Scholar
[97]
Richer, G.; Pister, A.; Abdelaal, M.; Fekete, J. D.; Sedlmair, M.; Weiskopf, D. Scalability in visualization. IEEE Transactions on Visualization and Computer Graphics , 2022.
Crossref Google Scholar
[98]
Dong, Q.; Li, L.; Dai, D.; Zheng, C.; Wu, Z.; Chang, B.; Sun, X.; Xu, J.; Li, L.; Sui, Z. A survey on in-context learning. arXiv preprint arXiv:2301.00234, 2022.
Google Scholar
[99]
Liu, S.; Xiao, J.; Liu, J.; Wang, X.; Wu, J.; Zhu, J. Visual diagnosis of tree boosting methods. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 163-173, 2018.
Crossref Google Scholar
[100]
Yuan, J.; Liu, M.; Tian, F.; Liu, S. Visual analysis of neural architecture spaces for summarizing design principles. IEEE Transactions on Visualization and Computer Graphics Vol. 29, No. 1, 288-298, 2023.
Crossref Google Scholar
[101]
Khayat, M.; Karimzadeh, M.; Zhao, J.; Ebert, D. S. VASSL: A visual analytics toolkit for social spambot labeling. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 874-883, 2020.
Crossref Google Scholar
[102]
Bernard, J.; Zeppelzauer, M.; Lehmann, M.; Müller, M.; Sedlmair, M. Towards user-centered active learning algorithms. Computer Graphics Forum Vol. 37, No. 3, 121-132, 2018.
Crossref Google Scholar
[103]
Yang, W.; Ye, X.; Zhang, X.; Xiao, L.; Xia, J.; Wang, Z.; Zhu, J.; Pfister, H.; Liu, S. Diagnosing ensemble few-shot classifiers. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 9, 3292-3306, 2022.
Crossref Google Scholar
[104]
Zhou, Z. H.; Tan, Z. H. Learnware: Small models do big. Science China Information Sciences Vol. 67, No. 1, Article No. 112102, 2023.
Crossref Google Scholar
[105]
HuggingFace. Available at https://huggingface.co/models
[106]
Wang, Q.; Yuan, J.; Chen, S.; Su, H.; Qu, H.; Liu, S. Visual genealogy of deep neural networks. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 11, 3340-3352, 2020.
Crossref Google Scholar
[107]
Cao, K.; Liu, M.; Su, H.; Wu, J.; Zhu, J.; Liu, S. Analyzing the noise robustness of deep neural networks. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 7, 3289-3304, 2021.
Crossref Google Scholar
[108]
Liu, M.; Liu, S.; Su, H.; Cao, K.; Zhu, J. Analyzing the noise robustness of deep neural networks. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 60-71, 2018.
Crossref
[109]
Qiu, R.; Tu, Y.; Wang, Y. S.; Yen, P. Y.; Shen, H. W. DocFlow: A visual analytics system for question-based document retrieval and categorization. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 2, 1533-1548, 2024.
Crossref Google Scholar
[110]
Shi, D.; Xu, X.; Sun, F.; Shi, Y.; Cao, N. Calliope: Automatic visual data story generation from a spreadsheet. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 453-463, 2021.
Crossref Google Scholar
[111]
Chen, Q.; Chen, N.; Shuai, W.; Wu, G.; Xu, Z.; Tong, H.; Cao, N. Calliope-net: Automatic generation of graph data facts via annotated node-link diagrams. IEEE Transactions on Visualization and Computer Graphics Vol. 30, No. 1, 562-572, 2024.
Crossref Google Scholar
[112]
Blei D. M.; Ng A. Y.; Jordan, M. I. Latent dirichlet allocation. Journal of Machine Learning Research Vol. 3, 993-1022, 2003.
Google Scholar
[113]
Lowe, D. G. Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision, 1150-1157, 1999.
Crossref
[114]
Rozière, B.; Gehring, J.; Gloeckle, F.; Sootla, S.; Gat, L.; Tan, X. E.; Adi, Y.; Liu, J.; Sauvestre, R.; Remez, T.; et al. Code Llama: Open foundation models for code. arXiv preprint arXiv:2308.12950, 2023.
Google Scholar
[115]
Bostock, M.; Ogievetsky, V.; Heer, J. D3 Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics Vol. 17, No. 12, 2301-2309, 2011.
Crossref Google Scholar
[116]
Hunter, J. D. Matplotlib: A 2D graphics environment. Computing in Science and Engineering Vol. 9, No. 3, 90-95, 2007.
Crossref Google Scholar
[117]
Kwon, O. H.; Ma, K. L. A deep generative model for graph layout. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 665-675, 2020.
Crossref Google Scholar
[118]
Zamfirescu-Pereira, J. D.; Wong, R. Y.; Hartmann, B.; Yang, Q. Why johnny can’t prompt: How non-AI experts try (and fail) to design LLM prompts. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Article No. 437, 2023.
Crossref
[119]
Pryzant, R.; Iter, D.; Li, J.; Lee, Y. T.; Zhu, C.; Zeng, M. Automatic prompt optimization with “gradient descent” and beam search. arXiv preprint arXiv:2305.03495, 2023.
Crossref Google Scholar
[120]
Jing, Y.; Yang, Y.; Feng, Z.; Ye, J.; Yu, Y.; Song, M. Neural style transfer: A review. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 11, 3365-3385, 2020.
Crossref Google Scholar
[121]
Abdal, R.; Qin, Y.; Wonka, P. Image2StyleGAN: How to embed images into the StyleGAN latent space? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 4432-4441, 2019.
Crossref
[122]
Chen, Q.; Cao, S.; Wang, J.; Cao, N. How does automation shape the process of narrative visualization: A survey of tools. IEEE Transactions on Visualization and Computer Graphics , 2023.
Crossref Google Scholar
[123]
Antol, S.; Agrawal, A.; Lu, J.; Mitchell, M.; Batra, D.; Zitnick, C. L.; Parikh, D. VQA: Visual question answering. In: Proceedings of the IEEE International Conference on Computer Vision, 2425-2433, 2015.
Crossref
[124]
Anil, R.; Dai, A. M.; Firat, O.; Johnson, M.; Lepikhin, D.; Passos, A.; Shakeri, S.; Taropa, E.; Bailey, P.; Chen, Z.; et al. PaLM 2 technical report. arXiv preprint arXiv:2305.10403, 2023.
Google Scholar
[125]
Zhao, Y.; Jiang, H.; Chen, Q. A.; Qin, Y.; Xie, H.; Wu, Y.; Liu, S.; Zhou, Z.; Xia, J.; Zhou, F. Preserving minority structures in graph sampling. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1698-1708, 2021.
Crossref Google Scholar
[126]
Yuan, J.; Xiang, S.; Xia, J.; Yu, L.; Liu, S. Evaluation of sampling methods for scatterplots. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1720-1730, 2021.
Crossref Google Scholar
[127]
Pan, X.; Tewari, A.; Leimkühler, T.; Liu, L.; Meka, A.; Theobalt, C. Drag your GAN: Interactive point-based manipulation on the generative image manifold. In: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference, Article No. 78, 2023.
Crossref
[128]
Wang, L.; Ma, C.; Feng, X.; Zhang, Z.; Yang, H.; Zhang, J.; Chen, Z.; Tang, J.; Chen, X.; Lin, Y.; et al. A survey on large language model based autonomous agents. arXiv preprint arXiv:2308.11432, 2023.
Crossref Google Scholar
[129]
Park, J. S.; O’Brien, J.; Cai, C. J.; Morris, M. R.; Liang, P.; Bernstein, M. S. Generative agents: Interactive simulacra of human behavior. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, Article No. 2, 2023.
Crossref
Computational Visual Media
Volume 10 Issue 3,
June 2024
Pages 399-424
DOI: 10.1007/s41095-023-0393-x
Cite this article:
Yang W, Liu M, Wang Z, et al. Foundation models meet visualizations: Challenges andopportunities. Computational Visual Media, 2024, 10(3): 399-424. https://doi.org/10.1007/s41095-023-0393-x

553

Views

13

Downloads

9

Crossref

5

Web of Science

17

Scopus

0

CSCD

Altmetrics
Received: 04 October 2023
Accepted: 15 November 2023
Published: 02 May 2024
© The Author(s) 2024.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
Return