IEEE Computer Graphics and Applications最新文献_第6页

Computational Design and Fabrication of Protective Foam. 防护泡沫的计算设计与制造。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-04-01 DOI: 10.1109/MCG.2025.3556656

Tsukasa Fukusato, Naoki Kita

引用次数: 0

Voting-Based Intervention Planning Using AI-Generated Images. 使用人工智能生成的图像进行基于投票的干预计划。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3553620

Ioannis Kavouras, Ioannis Rallis, Emmanuel Sardis, Anastasios Doulamis, Nikolaos Doulamis

引用次数: 0

Reflections on the Use of Dashboards in the COVID-19 Pandemic. 关于在COVID-19大流行期间使用仪表板的思考。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3538257

Alessio Arleo, Rita Borgo, Jorn Kohlhammer, Roy A Ruddle, H Scharlach, Xiaoru Yuan, Melanie Tory, Daniel Keefe

引用次数: 0

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics. LossLens：通过损失景观视觉分析进行机器学习诊断。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2024.3509374

Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Jeevan Chaudhari, John Kevin Cava, Michael W Mahoney, Talita Perciano, Gunther H Weber, Ross Maciejewski

{"title":"LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics.","authors":"Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Jeevan Chaudhari, John Kevin Cava, Michael W Mahoney, Talita Perciano, Gunther H Weber, Ross Maciejewski","doi":"10.1109/MCG.2024.3509374","DOIUrl":"10.1109/MCG.2024.3509374","url":null,"abstract":"Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. We demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network solving a simple convection problem.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":"112-125"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

HoloJig: Interactive Spoken Prompt Specified Generative AI Environments. HoloJig：交互式语音提示指定生成AI环境。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3553780

Llogari Casas, Samantha Hannah, Kenny Mitchell

引用次数: 0

Should I Render or Should AI Generate? Crafting Synthetic Semantic Segmentation Datasets With Controlled Generation. 我应该渲染还是AI生成？合成语义分割数据集与控制生成。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3553494

Omar A Mures, Manuel Silva, Manuel Lijo-Sanchez, Emilio J Padron, Jose A Iglesias-Guitian

{"title":"Should I Render or Should AI Generate? Crafting Synthetic Semantic Segmentation Datasets With Controlled Generation.","authors":"Omar A Mures, Manuel Silva, Manuel Lijo-Sanchez, Emilio J Padron, Jose A Iglesias-Guitian","doi":"10.1109/MCG.2025.3553494","DOIUrl":"10.1109/MCG.2025.3553494","url":null,"abstract":"This work explores the integration of generative AI models for automatically generating synthetic image-labeled data. Our approach leverages controllable diffusion models to generate synthetic variations of semantically labeled images. Synthetic datasets for semantic segmentation struggle to represent real-world subtleties, such as different weather conditions or fine details, typically relying on costly simulations and rendering. However, diffusion models can generate diverse images using input text prompts and guidance images, such as semantic masks. Our work introduces and tests a novel methodology for generating labeled synthetic images, with an initial focus on semantic segmentation, a demanding computer vision task. We showcase our approach in two distinct image segmentation domains, outperforming traditional computer graphics simulations in efficiently creating diverse datasets and training downstream models. We leverage generative models for crafting synthetically labeled images, posing the question: \"Should I render or should AI generate?\" Our results endorse a paradigm shift toward controlled generation models.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":"57-68"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Unified Visual Comparison Framework for Human and AI Paintings Using Neural Embeddings and Computational Aesthetics. 使用神经嵌入和计算美学的人类和人工智能绘画的统一视觉比较框架。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3555122

Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng

引用次数: 0

Visual Analytics for Explainable and Trustworthy Artificial Intelligence. 可解释和可信赖的人工智能的可视化分析。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3533806

Angelos Chatzimparmpas, Sumanta N Pattanaik

{"title":"Visual Analytics for Explainable and Trustworthy Artificial Intelligence.","authors":"Angelos Chatzimparmpas, Sumanta N Pattanaik","doi":"10.1109/MCG.2025.3533806","DOIUrl":"10.1109/MCG.2025.3533806","url":null,"abstract":"Our society increasingly depends on intelligent systems to solve complex problems, ranging from recommender systems suggesting the next movie to watch to AI models assisting in medical diagnoses for hospitalized patients. With the iterative improvement of diagnostic accuracy and efficiency, AI holds significant potential to mitigate medical misdiagnoses by preventing numerous deaths and reducing an economic burden of approximately € 450 billion annually. However, a key obstacle to AI adoption lies in the lack of transparency, that is, many automated systems provide predictions without revealing the underlying processes. This opacity can hinder experts' ability to trust and rely on AI systems. Visual analytics (VA) provides a compelling solution by combining AI models with interactive visualizations. These specialized charts and graphs empower users to incorporate their domain expertise to refine and improve the models, bridging the gap between AI and human understanding. In this work, the author defines, categorizes, and explores how VA solutions can foster trust across the stages of a typical AI pipeline. The author proposes a design space for innovative visualizations and presents an overview of our previously developed VA dashboards, which support critical tasks within the various pipeline stages, including data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"45 2","pages":"100-111"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Mixed Reality Collaboration: How Expert Representations Shape User Experiences. 混合现实协作：专家代表如何塑造用户体验。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3537316

Bernardo Marques, Samuel Silva, Carlos Ferreira, Sergio Oliveira, Andreia Santos, Paulo Dias, Beatriz Sousa Santos, Mike Potel

{"title":"Mixed Reality Collaboration: How Expert Representations Shape User Experiences.","authors":"Bernardo Marques, Samuel Silva, Carlos Ferreira, Sergio Oliveira, Andreia Santos, Paulo Dias, Beatriz Sousa Santos, Mike Potel","doi":"10.1109/MCG.2025.3537316","DOIUrl":"https://doi.org/10.1109/MCG.2025.3537316","url":null,"abstract":"Remote assistance has become integral in today's work environments, enabling offsite experts to guide team members in need of assistance. One technology that has gained momentum is mixed reality (MR), combining virtual and augmented reality elements, bridging physical distances, and facilitating knowledge sharing. One critical area is the representation of remote experts. Understanding how different representations influence the learning process is essential for improving the shared experience. Effective representation can enhance communication, trust, and the perceived expertise of the remote expert, thereby improving the learning and collaboration experiences. This study focuses on evaluating how different representations affect various dimensions of collaboration. The study involved 57 participants and examined six different conditions: no visual representation, representation using emojis, representation using a cartoon avatar, representation using a robot avatar, representation using a realistic avatar, and live stream using video.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"45 2","pages":"143-151"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Meet-in-Style: Text-Driven Real-Time Video Stylization Using Diffusion Models. 风格相遇：使用扩散模型的文本驱动的实时视频风格化。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-01 DOI: 10.1109/MCG.2025.3554312

David Kunz, Ondrej Texler, David Mould, Daniel Sykora

引用次数: 0