Foundation models meet visualizations: Challenges and opportunities

IF 17.3 3区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computational Visual Media Pub Date : 2024-05-02 DOI:10.1007/s41095-023-0393-x

Weikai Yang, Mengchen Liu, Zheng Wang, Shixia Liu

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Abstract

Recent studies have indicated that foundation models, such as BERT and GPT, excel at adapting to various downstream tasks. This adaptability 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 evaluating these intricate foundation models. VIS4FM addresses the pressing need for transparency, explainability, 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 opportunities, this study aims to provide a starting point for the continued exploration of this research avenue.

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基础模型与可视化的结合：挑战与机遇

最近的研究表明，BERT 和 GPT 等基础模型擅长适应各种下游任务。这种适应性使它们成为构建人工智能（AI）系统的主导力量。此外，随着可视化技术融入这些模型，一种新的研究范式也应运而生。本研究将这些交叉点分为两个研究领域：基础模型可视化（VIS4FM）和可视化基础模型（FM4VIS）。在 VIS4FM 方面，我们探讨了可视化在理解、完善和评估这些错综复杂的基础模型方面的主要作用。VIS4FM 解决了对透明度、可解释性、公平性和稳健性的迫切需求。相反，就 FM4VIS 而言，我们强调如何利用基础模型来推动可视化领域本身的发展。基础模型与可视化的交叉是大有可为的，但也带来了一系列挑战。通过强调这些挑战和有前途的机遇，本研究旨在为继续探索这一研究途径提供一个起点。

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来源期刊

Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

16.90

自引率

5.80%

发文量

243

审稿时长

6 weeks

期刊介绍： Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.