Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references. However, these approaches are effective only for common concepts, and are vulnerable to unstable image referencing and varying image conditions. Our formative study with designers underscores the need for primary-accent color compositions and context-dependent colors (e.g., 'clear' vs. 'polluted' sky) in design. In response, we introduce a generative approach for mining semantically resonant colors leveraging images generated by text-to-image models. Our insight is that contemporary text-to-image models can resemble visual patterns from large-scale real-world data. The framework comprises three stages: concept instancing produces generative samples using diffusion models, text-guided image segmentation identifies concept-relevant regions within the image, and color association extracts primarily accompanied by accent colors. Quantitative comparisons with expert designs validate our approach's effectiveness, and we demonstrate the applicability through cases in various design scenarios and a gallery.

Effective visualization is essential for cultural heritage interpretation. However, existing visualization systems remain constrained by fragmented data integration and limited exploration capabilities for multimodal heritage data. This paper presents HeritageExplorer, an interactive system that synergizes large language models (LLMs) with dynamic visualizations to enable progressive heritage exploration. Our approach constructs a comprehensive knowledge graph integrating 831 historic buildings in Guangzhou, which unifies their architectural, spatial, temporal, and contextual attributes. The system’s novel integration of KG-enhanced contextual understanding with LLMs supports: natural language query understanding and seamless coupling with interactive visualizations. Quantitative evaluation demonstrates consistent improvements in factual accuracy across heritage tasks, while case studies illustrate its successful application in diverse exploration scenarios.

Dashboards serve as effective visualization tools for conveying complex information. However, there exists a knowledge gap regarding how dashboard designs impact user engagement, necessitating designers to rely on their design expertise. Saliency has been used to comprehend viewing behaviors and assess visualizations, yet existing saliency models are primarily designed for single-view visualizations. To address this, we conduct an eye-tracking study to quantify participants' viewing patterns on dashboards. We collect eye-movement data from 60 participants, each viewing 36 dashboards (16 representative dashboards shared across all and 20 unique to each participant), totaling 1,216 dashboards and 2,160 eye-movement data instances. Analysis of the data from 16 dashboards viewed by all participants provides insights into how dashboard objects and layout designs influence viewing behaviors. Our analysis confirms known viewing patterns and reveals new patterns related to dashboard layout designs. Using the eye-movement data and identified patterns, we develop a saliency model to predict viewing behaviors with dashboards. Compared to state-of-the-art models for single-view visualizations, our model demonstrates overall improvement in prediction performance for dashboards. Finally, we propose potential dashboard design guidelines, illustrate an application case, and discuss general scanning strategies along with limitations and future work.

Interior color design is a creative process that endeavors to allocate colors to furniture and other elements within an interior space. While much research focuses on generating realistic interior designs, these automated approaches often misalign with user intention and disregard design rationales. Informed by a need-finding preliminary study, we develop C2Ideas, an innovative system for designers to creatively ideate color schemes enabled by an intent-aligned and domain-oriented large language model. C2Ideas integrates a three-stage process: Idea Prompting stage distills user intentions into color linguistic prompts; Word-Color Association stage transforms the prompts into semantically and stylistically coherent color schemes; and Interior Coloring stage assigns colors to interior elements complying with design principles. We also develop an interactive interface that enables flexible user refinement and interpretable reasoning. C2Ideas has undergone a series of indoor cases and user studies, demonstrating its effectiveness and high recognition of interactive functionality by designers.

Multiple-view (MV) visualizations have become ubiquitous for visual communication and exploratory data visualization. However, most existing MV visualizations are designed for the desktop, which can be unsuitable for the continuously evolving displays of varying screen sizes. In this article, we present a two-stage adaptation framework that supports the automated retargeting and semi-automated tailoring of a desktop MV visualization for rendering on devices with displays of varying sizes. First, we cast layout retargeting as an optimization problem and propose a simulated annealing technique that can automatically preserve the layout of multiple views. Second, we enable fine-tuning for the visual appearance of each view, using a rule-based auto configuration method complemented with an interactive interface for chart-oriented encoding adjustment. To demonstrate the feasibility and expressivity of our proposed approach, we present a gallery of MV visualizations that have been adapted from the desktop to small displays. We also report the result of a user study comparing visualizations generated using our approach with those by existing methods. The outcome indicates that the participants generally prefer visualizations generated using our approach and find them to be easier to use.

Generative AI (GenAI) has witnessed remarkable progress in recent years and demonstrated impressive performance in various generation tasks in different domains such as computer vision and computational design. Many researchers have attempted to integrate GenAI into visualization framework, leveraging the superior generative capacity for different operations. Concurrently, recent major breakthroughs in GenAI like diffusion model and large language model have also drastically increase the potential of GenAI4VIS. From a technical perspective, this paper looks back on previous visualization studies leveraging GenAI and discusses the challenges and opportunities for future research. Specifically, we cover the applications of different types of GenAI methods including sequence, tabular, spatial and graph generation techniques for different tasks of visualization which we summarize into four major stages: data enhancement, visual mapping generation, stylization and interaction. For each specific visualization sub-task, we illustrate the typical data and concrete GenAI algorithms, aiming to provide in-depth understanding of the state-of-the-art GenAI4VIS techniques and their limitations. Furthermore, based on the survey, we discuss three major aspects of challenges and research opportunities including evaluation, dataset, and the gap between end-to-end GenAI methods and visualizations. By summarizing different generation algorithms, their current applications and limitations, this paper endeavors to provide useful insights for future GenAI4VIS research.

Abstract Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing example‐based chart retrieval methods are built upon non‐decoupled and low‐level visual features that are hard to interpret, while definition‐based ones are constrained to pre‐defined attributes that are hard to extend. In this work, we propose a new framework, namelyWYTIWYR (What‐You‐Think‐Is‐What‐You‐Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, theAnnotationstage disentangles the visual attributes within the query chart; and second, theRetrievalstage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop aprototypeWYTIWYRsystem leveraging a contrastive language‐image pre‐training (CLIP) model to achieve zero‐shot classification as well as multi‐modal input encoding, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.