IEEE transactions on visualization and computer graphics最新文献

Data-driven decision making has become a popular practice in science, industry, and public policy. Yet data alone, as an imperfect and partial representation of reality, is often insufficient to make good analysis decisions. Knowledge about the context of a dataset, its strengths and weaknesses, and its applicability for certain tasks is essential. Analysts are often not only familiar with the data itself, but also have data hunches about their analysis subject. In this work, we present an interview study with analysts from a wide range of domains and with varied expertise and experience, inquiring about the role of contextual knowledge. We provide insights into how data is insufficient in analysts' workflows and how they incorporate other sources of knowledge into their analysis. We analyzed how knowledge of data shaped their analysis outcome. Based on the results, we suggest design opportunities to better and more robustly consider both knowledge and data in analysis processes.

Scatterplots are widely used across various domains to identify anomalies in datasets, particularly in multi-class settings, such as detecting misclassified or mislabeled data. However, scatterplot effectiveness often declines with large datasets due to limited display resolution. This paper introduces a novel Visual Quality Measure (VQM) - OM4AnI (Overlap Measure for Anomaly Identification) - which quantifies the degree of overlap for identifying anomalies, helping users estimate how effectively anomalies can be observed in multi-class scatterplots. OM4AnI begins by computing anomaly index based on each data point's position relative to its class cluster. The scatterplot is then discretized into a matrix representation by binning the display space into cell-level (pixel-level) grids and computing the coverage for each pixel. It takes into account the anomaly index of data points covering these pixels and visual features (marker shapes, marker sizes, and rendering orders). Building on this foundation, we sum all the coverage information in each cell (pixel) of matrix representation to obtain the final quality score with respect to anomaly identification. We conducted an evaluation to analyze the efficiency, effectiveness, sensitivity of OM4AnI in comparison with six representative baseline methods that are based on different computation granularity levels: data level, marker level, and pixel level. The results show that OM4AnI outperforms baseline methods by exhibiting more monotonic trends against the ground truth and greater sensitivity to rendering order, unlike the baseline methods. It confirms that OM4AnI can inform users about how effectively their scatterplots support anomaly identification. Overall, OM4AnI shows strong potential as an evaluation metric and for optimizing scatterplots through automatic adjustment of visual parameters.

Text labels are widely used to convey auxiliary information in visualization and graphic design. The substantial variability in the categories and structures of labeled objects leads to diverse label layouts. Recent single-model learning-based solutions in label placement struggle to capture fine-grained differences between these layouts, which in turn limits their performance. In addition, although human designers often consult previous works to gain design insights, existing label layouts typically serve merely as training data, limiting the extent to which embedded design knowledge can be exploited. To address these challenges, we propose a mixture of cluster-guided experts (MoCE) solution for label placement. In this design, multiple experts jointly refine layout features, with each expert responsible for a specific cluster of layouts. A cluster-based gating function assigns input samples to experts based on representation clustering. We implement this idea through the Label Placement Cluster-guided Experts (LPCE) model, in which a MoCE layer integrates multiple feed-forward networks (FFNs), with each expert composed of a pair of FFNs. Furthermore, we introduce a retrieval augmentation strategy into LPCE, which retrieves and encodes reference layouts for each input sample to enrich its representations. Extensive experiments demonstrate that LPCE achieves superior performance in label placement, both quantitatively and qualitatively, surpassing a range of state-of-the-art baselines. Our algorithm is available at https://github.com/PingshunZhang/LPCE.

Zero-shot text-to-video diffusion models are crafted to expand pre-trained image diffusion models to the video domain without additional training. In recent times, prevailing techniques commonly rely on existing shapes as constraints and introduce inter-frame attention to ensure texture consistency. However, such shape constraints tend to restrict the stylized geometric deformation of videos and inadvertently neglect the original texture characteristics. Furthermore, existing methods suffer from flickering and inconsistent facial expressions. In this paper, we present DiffPortraitVideo. The framework employs a diffusion model-based feature and attention injection mechanism to generate key frames, with cross-frame constraints to enforce coherence and adaptive feature fusion to ensure expression consistency. Our approach achieves high spatio-temporal and expression consistency while retaining the textual and original image properties. Extensive and comprehensive experiments are conducted to validate the efficacy of our proposed framework in generating personalized, high-quality, and coherent videos. This not only showcases the superiority of our method over existing approaches but also paves the way for further research and development in the field of text-to-video generation with enhanced personalization and quality.

Sentiment contagion occurs when attitudes toward one topic are influenced by attitudes toward others. Detecting and understanding this phenomenon is essential for analyzing topic evolution and informing social policies. Prior research has developed models to simulate the contagion process through hypothesis testing and has visualized user-topic correlations to aid comprehension. Nevertheless, the vast volume of topics and the complex interrelationships on social media present two key challenges: (1) efficient construction of large-scale sentiment contagion networks, and (2) in-depth explorations of these networks. To address these challenges, we introduce a causality-based framework that efficiently constructs and explains sentiment contagion. We further propose a map-like visualization technique that encodes time using a horizontal axis, enabling efficient visualization of causality-based sentiment flow while maintaining scalability through limitless spatial segmentation. Based on the visualization, we develop CausalMap, a system that supports analysts in tracing sentiment contagion pathways and assessing the influence of different demographic groups. Furthermore, we conduct comprehensive evaluations--including two use cases, a task-based user study, an expert interview, and an algorithm evaluation--to validate the usability and effectiveness of our approach.

Although data visualization is powerful for revealing patterns and communicating insights, creating effective visualizations requires familiarity with authoring tools and often disrupts the analysis flow. While large language models show promise for automatically converting analysis intent into visualizations, existing methods function as black boxes without transparent reasoning processes, which prevents users from understanding design rationales and refining suboptimal outputs. To bridge this gap, we propose integrating Chain-of-Thought (CoT) reasoning into the Natural Language to Visualization (NL2VIS) pipeline. First, we design a comprehensive CoT reasoning process for NL2VIS and develop an automatic pipeline to equip existing datasets with structured reasoning steps. Second, we introduce nvBench-CoT, a specialized dataset capturing detailed step-by-step reasoning from ambiguous natural language descriptions to finalized visualizations, which enables state-of-the-art performance when used for model fine-tuning. Third, we develop DeepVIS, an interactive visual interface that tightly integrates with the CoT reasoning process, allowing users to inspect reasoning steps, identify errors, and make targeted adjustments to improve visualization outcomes. Quantitative benchmark evaluations, two use cases, and a user study collectively demonstrate that our CoT framework effectively enhances NL2VIS quality while providing insightful reasoning steps to users.

Tactile graphics are widely used to present maps and statistical diagrams to blind and low vision (BLV) people, with accessibility guidelines recommending their use for graphics where spatial relationships are important. Their use is expected to grow with the advent of commodity refreshable tactile displays. However, in stark contrast to visual information graphics, we lack a clear understanding of the benefts that well-designed tactile information graphics offer over text descriptions for BLV people. To address this gap, we introduce a framework considering the three components of encoding, perception and cognition to examine the known benefts for visual information graphics and explore their applicability to tactile information graphics. This work establishes a preliminary theoretical foundation for the tactile-frst design of information graphics and identifes future research avenues.

We present VizGenie, a self-improving, agentic framework that advances scientific visualization through large language model (LLM) by orchestrating of a collection of domain-specific and dynamically generated modules. Users initially access core functionalities-such as threshold-based filtering, slice extraction, and statistical analysis-through pre-existing tools. For tasks beyond this baseline, VizGenie autonomously employs LLMs to generate new visualization scripts (e.g., VTK Python code), expanding its capabilities on-demand. Each generated script undergoes automated backend validation and is seamlessly integrated upon successful testing, continuously enhancing the system's adaptability and robustness. A distinctive feature of VizGenie is its intuitive natural language interface, allowing users to issue high-level feature-based queries (e.g., "visualize the skull" or "highlight tissue boundaries"). The system leverages image-based analysis and visual question answering (VQA) via fine-tuned vision models to interpret these queries precisely, bridging domain expertise and technical implementation. Additionally, users can interactively query generated visualizations through VQA, facilitating deeper exploration. Reliability and reproducibility are further strengthened by Retrieval-Augmented Generation (RAG), providing context-driven responses while maintaining comprehensive provenance records. Evaluations on complex volumetric datasets demonstrate significant reductions in cognitive overhead for iterative visualization tasks. By integrating curated domain-specific tools with LLM-driven flexibility, VizGenie not only accelerates insight generation but also establishes a sustainable, continuously evolving visualization practice. The resulting platform dynamically learns from user interactions, consistently enhancing support for feature-centric exploration and reproducible research in scientific visualization.

Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five state-of the-art visualization tools (LIME, SHAP, CP, Anchors, and ELI5) for model explainability, measuring how taxonomy characteristics affect comprehension, bias perception, and trust for non-expert ML users. Surprisingly, we find an inverse relationship between comprehension and trust: the better users understand the models, the less they trust them. We investigate the cause and find that this relationship is strongly mediated by bias perception: more comprehensible visualizations increase people's perception of bias, and increased bias perception reduces trust. We confirm this relationship is causal: Manipulating explainability visualizations to control comprehension, bias perception, and trust, we show that visualization design can significantly (p < 0.001) increase comprehension, increase perceived bias, and reduce trust. Conversely, reducing perceived model bias, either by improving model fairness or by adjusting visualization design, significantly increases trust even when comprehension remains high. Our work advances understanding of how comprehension affects trust and systematically investigates visualization's role in facilitating responsible ML applications.

Empirical research on perception and cognition has laid the foundation for visualization design, often distilled into practical guidelines intended to support effective chart creation. However, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the research-practice gap in visualization design guidelines through a mixed-methods approach. We first collected design guidelines from practitioner-facing sources and empirical studies from academic venues to assess their alignment. To complement this analysis, we conducted surveys and interviews with practitioners and researchers to examine their experiences, perceptions, and challenges surrounding the development and use of design guidelines. Our findings reveal misalignment between empirical evidence and widely used guidelines, differing perspectives between communities, and key barriers that contribute to the persistence of the research-practice gap.