Visual Informatics最新文献

Visual analysis of LLM-based entity resolution from scientific papers 科学论文中基于llm的实体解析可视化分析

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-06-01 DOI: 10.1016/j.visinf.2025.100236

Siyu Wu , Yi Yang , Weize Wu , Ruiming Li , Yuyang Zhang , Ge Wang , Huobin Tan , Zipeng Liu , Lei Shi

{"title":"Visual analysis of LLM-based entity resolution from scientific papers","authors":"Siyu Wu , Yi Yang , Weize Wu , Ruiming Li , Yuyang Zhang , Ge Wang , Huobin Tan , Zipeng Liu , Lei Shi","doi":"10.1016/j.visinf.2025.100236","DOIUrl":"10.1016/j.visinf.2025.100236","url":null,"abstract":"<div><div>This paper focuses on the visual analytics support for extracting domain-specific entities from extensive scientific literature, a task with inherent limitations using traditional named entity resolution methods. With the advent of large language models (LLMs) such as GPT-4, significant improvements over conventional machine learning approaches have been achieved due to LLM’s capability on entity resolution integrate abilities such as understanding multiple types of text. This research introduces a new visual analysis pipeline that integrates these advanced LLMs with versatile visualization and interaction designs to support batch entity resolution. Specifically, we focus on a specific material science field of Metal-Organic Frameworks (MOFs) and a large data collection namely CSD-MOFs. Through collaboration with domain experts in material science, we obtain well-labeled synthesis paragraphs. We propose human-in-the-loop refinement over the entity resolution process using visual analytics techniques, which allows domain experts to interactively integrate insights into LLM intelligence, including error analysis and interpretation of the retrieval-augmented generation (RAG) algorithm. Our evaluation through the case study of example selection for RAG demonstrates that this visual analysis approach effectively improves the accuracy of single-document entity resolution.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100236"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YOLO-SAATD: An efficient SAR airport and aircraft target detector YOLO-SAATD：高效的SAR机场和飞机目标探测器

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-04-16 DOI: 10.1016/j.visinf.2025.100240

Daobin Ma , Zhanhong Lu , Zixuan Dai , Yangyue Wei , Li Yang , Haimiao Hu , Wenqiao Zhang , Dongping Zhang

{"title":"YOLO-SAATD: An efficient SAR airport and aircraft target detector","authors":"Daobin Ma , Zhanhong Lu , Zixuan Dai , Yangyue Wei , Li Yang , Haimiao Hu , Wenqiao Zhang , Dongping Zhang","doi":"10.1016/j.visinf.2025.100240","DOIUrl":"10.1016/j.visinf.2025.100240","url":null,"abstract":"<div><div>While object detection performs well in natural images, it faces challenges in Synthetic Aperture Radar (SAR) images for detecting airports and aircraft due to discrete scattering points, complex backgrounds, and multi-scale targets. Existing methods struggle with computational inefficiency, omission of small targets, and low accuracy. We propose a SAR airport and aircraft target detection model based on YOLO, named YOLO-SAATD (You Only Look Once-SAR Airport and Aircraft Target Detector), which tackles the aforementioned challenges from three perspectives: <strong>1. Efficiency</strong>: A lightweight hierarchical multi-scale backbone reduces parameters and enhances detection speed. <strong>2. Fine granularity</strong>: A ”ScaleNimble Neck” integrates feature reshaping and scale-aware aggregation to enhance detail detection and feature capture in multi-scale SAR images. <strong>3. Precision</strong>: Wise-IoU loss function is used to optimize bounding box localization and enhance detection accuracy. Experiments on the SAR-Airport-1.0 and SAR-AirCraft-1.0 datasets show that YOLO-SAATD improves precision and mAP50 by 1%-2%, increases detection frame rate by 15%, and reduces model parameters by 25% compared to YOLOv8n, thus validating its effectiveness for SAR airport and aircraft target detection.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100240"},"PeriodicalIF":3.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Photogrammetry engaged automated image labeling approach 摄影测量采用自动图像标记方法

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-04-09 DOI: 10.1016/j.visinf.2025.100239

Jonathan Boyack , Jongseong Brad Choi

{"title":"Photogrammetry engaged automated image labeling approach","authors":"Jonathan Boyack , Jongseong Brad Choi","doi":"10.1016/j.visinf.2025.100239","DOIUrl":"10.1016/j.visinf.2025.100239","url":null,"abstract":"<div><div>Deep learning models require many instances of training data to be able to accurately detect the desired object. However, the labeling of images is currently conducted manually due to the inclusion of irrelevant scenes in the original images, especially for the data collected in a dynamic environment such as from drone imagery. In this work, we developed an automated extraction of training data set using photogrammetry. This approach works with continuous and arbitrary collection of visual data, such as video, encompassing a stationary object. A dense point cloud was first generated to estimate the geometric relationship between individual images using a structure-from-motion (SfM) technique, followed by user-designated region-of-interests, ROIs, that are automatically extracted from the original images. An orthophoto mosaic of the façade plane of the building shown in the point cloud was created to ease the user’s selection of an intended labeling region of the object, which is a one-time process. We verified this method by using the ROIs extracted from a previously obtained dataset to train and test a convolutional neural network which is modeled to detect damage locations. The method put forward in this work allows a relatively small amount of labeling to generate a large amount of training data. We successfully demonstrate the capabilities of the technique with the dataset previously collected by a drone from an abandoned building in which many of the glass windows have been damaged.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100239"},"PeriodicalIF":3.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative object insertion in Gaussian splatting with a multi-view diffusion model 基于多视图扩散模型的高斯溅射生成对象插入

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-04-08 DOI: 10.1016/j.visinf.2025.100238

Hongliang Zhong, Can Wang, Jingbo Zhang, Jing Liao

{"title":"Generative object insertion in Gaussian splatting with a multi-view diffusion model","authors":"Hongliang Zhong, Can Wang, Jingbo Zhang, Jing Liao","doi":"10.1016/j.visinf.2025.100238","DOIUrl":"10.1016/j.visinf.2025.100238","url":null,"abstract":"<div><div>Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality results. To address this, we propose a novel method for object insertion in 3D content represented by Gaussian Splatting. Our approach introduces a multi-view diffusion model, dubbed MVInpainter, which is built upon a pre-trained stable video diffusion model to facilitate view-consistent object inpainting. Within MVInpainter, we incorporate a ControlNet-based conditional injection module to enable controlled and more predictable multi-view generation. After generating the multi-view inpainted results, we further propose a mask-aware 3D reconstruction technique to refine Gaussian Splatting reconstruction from these sparse inpainted views. By leveraging these fabricate techniques, our approach yields diverse results, ensures view-consistent and harmonious insertions, and produces better object quality. Extensive experiments demonstrate that our approach outperforms existing methods.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100238"},"PeriodicalIF":3.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual analysis of multi-subject association patterns in high-dimensional time-varying student performance data 高维时变学生成绩数据中多学科关联模式的可视化分析

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-04-07 DOI: 10.1016/j.visinf.2025.100237

Lianen Ji, Ziyi Wang, Shirong Qiu, Guang Yang, Sufang Zhang

{"title":"Visual analysis of multi-subject association patterns in high-dimensional time-varying student performance data","authors":"Lianen Ji, Ziyi Wang, Shirong Qiu, Guang Yang, Sufang Zhang","doi":"10.1016/j.visinf.2025.100237","DOIUrl":"10.1016/j.visinf.2025.100237","url":null,"abstract":"<div><div>Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a college undergraduate major. However, these high-dimensional time-varying student performance data involve multiple associated subjects, such as student, course, and teacher, which exhibit complex interrelationships in academic semesters, knowledge categories, and student groups. This makes it challenging to conduct a comprehensive analysis of association patterns. To this end, we construct a visual analysis framework, called MAPVis, to support multi-method and multi-level interactive exploration of the association patterns in student performance. MAPVis consists of two stages: in the first stage, we extract students’ learning patterns and further introduce mutual information to explore the distribution of learning patterns; in the second stage, various learning patterns and subject attributes are integrated based on a hierarchical apriori algorithm to achieve a multi-subject interactive exploration of the association patterns among students, courses, and teachers. Finally, we conduct a case study using real student performance data to verify the applicability and effectiveness of MAPVis.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100237"},"PeriodicalIF":3.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VisMocap: Interactive visualization and analysis for multi-source motion capture data VisMocap：多源动作捕捉数据的交互式可视化和分析

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-03-25 DOI: 10.1016/j.visinf.2025.100235

Lishuang Zhan , Rongting Li , Rui Cao , Juncong Lin , Shihui Guo

{"title":"VisMocap: Interactive visualization and analysis for multi-source motion capture data","authors":"Lishuang Zhan , Rongting Li , Rui Cao , Juncong Lin , Shihui Guo","doi":"10.1016/j.visinf.2025.100235","DOIUrl":"10.1016/j.visinf.2025.100235","url":null,"abstract":"<div><div>With the rapid advancement of artificial intelligence, research on enabling computers to assist humans in achieving intelligent augmentation—thereby enhancing the accuracy and efficiency of information perception and processing—has been steadily evolving. Among these developments, innovations in human motion capture technology have been emerging rapidly, leading to an increasing diversity in motion capture data types. This diversity necessitates the establishment of a unified standard for multi-source data to facilitate effective analysis and comparison of their capability to represent human motion. Additionally, motion capture data often suffer from significant noise, acquisition delays, and asynchrony, making their effective processing and visualization a critical challenge. In this paper, we utilized data collected from a prototype of flexible fabric-based motion capture clothing and optical motion capture devices as inputs. Time synchronization and error analysis between the two data types were conducted, individual actions from continuous motion sequences were segmented, and the processed results were presented through a concise and intuitive visualization interface. Finally, we evaluated various system metrics, including the accuracy of time synchronization, data fitting error from fabric resistance to joint angles, precision of motion segmentation, and user feedback.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100235"},"PeriodicalIF":3.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contextualized visual analytics for multivariate events 多变量事件的上下文可视化分析

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-03-21 DOI: 10.1016/j.visinf.2025.100234

Lei Peng , Ziyue Lin , Natalia Andrienko , Gennady Andrienko , Siming Chen

引用次数: 0

CodeLin: An in situ visualization method for understanding data transformation scripts CodeLin：一种用于理解数据转换脚本的现场可视化方法

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-03-19 DOI: 10.1016/j.visinf.2025.03.002

Xiwen Cai , Kai Xiong , Zhongsu Luo , Di Weng , Shuainan Ye , Yingcai Wu

{"title":"CodeLin: An in situ visualization method for understanding data transformation scripts","authors":"Xiwen Cai , Kai Xiong , Zhongsu Luo , Di Weng , Shuainan Ye , Yingcai Wu","doi":"10.1016/j.visinf.2025.03.002","DOIUrl":"10.1016/j.visinf.2025.03.002","url":null,"abstract":"<div><div>Understanding data transformation scripts is an essential task for data analysts who write code to process data. However, this can be challenging, especially when encountering unfamiliar scripts. Comments can help users understand data transformation code, but well-written comments are not always present. Visualization methods have been proposed to help analysts understand data transformations, but they generally require a separate view, which may distract users and entail efforts for connecting visualizations and code. In this work, we explore the use of in situ program visualization to help data analysts understand data transformation scripts. We present CodeLin, a new visualization method that combines word-sized glyphs for presenting transformation semantics and a lineage graph for presenting data lineage in an in situ manner. Through a use case, code pattern demonstrations, and a preliminary user study, we demonstrate the effectiveness and usability of CodeLin. We further discuss how visualization can help users understand data transformation code.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 2","pages":"Article 100233"},"PeriodicalIF":3.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key-isovalue selection and hierarchical exploration visualization of weather forecast ensembles 天气预报集合的键等值选择与分层探索可视化

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-03-01 DOI: 10.1016/j.visinf.2025.02.001

Feng Zhou, Hao Hu, Fengjie Wang, Jiamin Zhu, Wenwen Gao, Min Zhu

{"title":"Key-isovalue selection and hierarchical exploration visualization of weather forecast ensembles","authors":"Feng Zhou, Hao Hu, Fengjie Wang, Jiamin Zhu, Wenwen Gao, Min Zhu","doi":"10.1016/j.visinf.2025.02.001","DOIUrl":"10.1016/j.visinf.2025.02.001","url":null,"abstract":"<div><div>Weather forecast ensembles are commonly used to assess the uncertainty and confidence of weather predictions. Conventional methods in meteorology often employ ensemble mean and standard deviation plots, as well as spaghetti plots, to visualize ensemble data. However, these methods suffer from significant information loss and visual clutter. In this paper, we propose a new approach for uncertainty visualization of weather forecast ensembles, including isovalue selection based on information loss and hierarchical visualization that integrates visual abstraction and detail preservation. Our approach uses non-uniform downsampling to select key-isovalues and provides an interactive visualization method based on hierarchical clustering. Firstly, we sample key-isovalues by contour probability similarity and determine the optimal sampling number using an information loss curve. Then, the corresponding isocontours are presented to guide users in selecting key-isovalues. Once the isovalue is chosen, we perform agglomerative hierarchical clustering on the isocontours based on signed distance fields and generate visual abstractions for each isocontour cluster to avoid visual clutter. We link a bubble tree to the visual abstractions to explore the details of isocontour clusters at different levels. We demonstrate the utility of our approach through two case studies with meteorological experts on real-world data. We further validate its effectiveness by quantitatively assessing information loss and visual clutter. Additionally, we confirm its usability through expert evaluation.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"9 1","pages":"Pages 58-70"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A human-centric perspective on interpretability in large language models 大型语言模型中以人为中心的可解释性观点

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2025-03-01 DOI: 10.1016/j.visinf.2025.03.001

Zihan Zhou, Minfeng Zhu, Wei Chen

引用次数: 0