Supporting Expressive and Faithful Pictorial Visualization Design With Visual Style Transfer

Shi, Yang; Liu, Pei; Chen, Siji; Sun, Mengdi; Cao, Nan

doi:10.1109/tvcg.2022.3209486

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…In terms of data binding type, we have identified seven common data binding types used in data analogies. Four of the seven data binding types have been included in previous research [47], namely length, area, volume, and quantity. These four types are also common measurements that are easily depicted in static illustrations.…”

Section: Data Analogy Designmentioning

confidence: 99%

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Chen,

Shuai,

Zhang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Fig. 1. An example of the AnalogyMate system automatically suggests data analogy for the inputs. The system first suggests data analogies according to the input, then generates corresponding design solutions and visual representations for illustrations. Finally, we show a user-rendered version of one of the analogy designs. Unfamiliar measurements usually hinder readers from grasping the scale of the numerical data, understanding the content, and feeling engaged with the context. To enhance data comprehension and communication, we leverage analogies to bridge the gap between abstract data and familiar measurements. In this work, we first conduct semi-structured interviews with design experts to identify design problems and summarize design considerations. Then, we collect an analogy dataset of 138 cases from various online sources. Based on the collected dataset, we characterize a design space for creating data analogies. Next, we build a prototype system, AnalogyMate, that automatically suggests data analogies, their corresponding design solutions, and generated visual representations powered by generative AI. The study results show the usefulness of AnalogyMate in aiding the creation process of data analogies and the effectiveness of data analogy in enhancing data comprehension and communication.

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Section: Data Analogy Designmentioning

confidence: 99%

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Chen,

Shuai,

Zhang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…This enables the generation of different graph layouts. Vistylist (Shi et al., 2022) uses object detection to extract various elements of charts and compares their similarity in a latent space using a Siamese Neural Network (Lagunas et al., 2019). This allows for style transfer by replacing the elements with those in a database that have similar styles.…”

Section: Related Workmentioning

confidence: 99%

Interactive visual query of density maps on latent space via flow‐based models

Li,

Liang,

Jiang

et al. 2024

Transactions in GIS

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Visual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency.

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“…Recent studies ( e.g. , [MMA18, KRS * 21, LCF * 15, SLC * 23]) stress the effects of chart attributes on users' comprehension and cognitive loads. Specifically, Li et al [LCF * 15] point out that two charts of the same data but with different layouts can cause perceptional imbalance, even though they are informatively equivalent.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, Li et al [LCF * 15] point out that two charts of the same data but with different layouts can cause perceptional imbalance, even though they are informatively equivalent. Despite the importance, the design space of chart attributes is too large for designers to choose from, resulting in various types of charts in designers' own style [SLC * 23]. Most of these works follow a coarse‐to‐fine strategy to first identify chart types and then extract visual marks and channels [SKC * 11,JKS * 17,PMH17,YZF * 22].…”

Section: Related Workmentioning

confidence: 99%

WYTIWYR: A User Intent‐Aware Framework with Multi‐modal Inputs for Visualization Retrieval

Xiao

Hou

Cheng

et al. 2023

Computer Graphics Forum

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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, namely WYTIWYR (What‐You‐Think‐Is‐What‐You‐Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, the Annotation stage disentangles the visual attributes within the query chart; and second, the Retrieval stage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop aprototype WYTIWYR system 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.

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Supporting Expressive and Faithful Pictorial Visualization Design With Visual Style Transfer

Cited by 13 publications

References 47 publications

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Interactive visual query of density maps on latent space via flow‐based models

WYTIWYR: A User Intent‐Aware Framework with Multi‐modal Inputs for Visualization Retrieval

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