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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Sign Pose-based Transformer for Word-level Sign Language Recognition

Sign Pose-based Transformer for Word-level Sign Language Rec...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Bohacek, Matyas Hruz, Marek Univ West Bohemia Fac Appl Sci Dept Cybernet & New Technol Informat Soc Tech 8 Plzen 30100 Czech Republic

ISBN: (纸本)9781665458245

In this paper we present a system for word-level sign language recognition based on the Transformer model. We aim at a solution with low computational cost, since we see great potential in the usage of such recognition system on hand-held devices. We base the recognition on the estimation of the pose of the human body in the form of 2D landmark locations. We introduce a robust pose normalization scheme which takes the signing space in consideration and processes the hand poses in a separate local coordinate system, independent on the body pose. We show experimentally the significant impact of this normalization on the accuracy of our proposed system. We introduce several augmentations of the body pose that further improve the accuracy, including a novel sequential joint rotation augmentation. With all the systems in place, we achieve state of the art top-1 results on the WLASL and LSA64 datasets. For WLASL, we are able to successfully recognize 63.18 % of sign recordings in the 100-gloss subset, which is a relative improvement of 5 % from the prior state of the art. For the 300-gloss subset, we achieve recognition rate of 43.78 % which is a relative improvement of 3.8 %. With the LSA64 dataset, we report test recognition accuracy of 100 %.

关键词： Training visualization Computational modeling Gesture recognition Assistive technologies Transformers Data models

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Reasoning and evolution of perceptions. The Influence of Color, on Brand's Logo in Malaysia 23

Reasoning and evolution of perceptions. The Influence of Col...

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23rd ieee International conference on High Performance Computing and Communications, 7th ieee International conference on Data Science and Systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Rasch, Firend Al. Mosteanu, Narcisa Roxana Sheng, Lee Yee College of Business American University of Malta Malta Malta Universiti Putra Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781665494571

As visual equity contributes to brand recognition, reasoning and evolution of perception tends to empower brands. Research objectives are to examine the associations of consumer's perception of color and brand logo possible influences and the evolution of that perception. The results of data analyses shows that the three hypotheses in which consumers will apply conventional color associations to the color of a brand's logo;consumers will apply personality traits to a brand, using color in the brand's logo, and consumers view brand logo as more appropriate when brand personality and color associations are congruent. Data analyses further reveals that customers tend to associate colors with personality traits that would affect their perception towards a brand and its logo within the South-East Asian context. © 2021 ieee.

关键词： visualization Data analysis Image color analysis Smart cities Education Organizations Color

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ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework

ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Reg...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Mo, Ningkai Gan, Wanshui Yokoya, Naoto Chen, Shifeng Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Univ Tokyo Tokyo Japan RIKEN Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects. This framework is designed in a simple architecture that efficiently extracts point-wise features from RGB-D data using a fully convolutional network, called XYZNet, and directly regresses the 6D pose without any post refinement. In the case of symmetric object, one object has multiple ground-truth poses, and this one-to-many relationship may lead to estimation ambiguity. In order to solve this ambiguity problem, we design a symmetry-invariant pose distance metric, called average (maximum) grouped primitives distance or A(M)GPD. The proposed A(M)GPD loss can make the regression network converge to the correct state, i.e., all minima in the A(M)GPD loss surface are mapped to the correct poses. Extensive experiments on YCB-Video and TLESS datasets demonstrate the proposed framework's substantially superior performance in top accuracy and low computational cost. The relevant code is available in https://***/GANWANSHUI/***.

关键词： Convolutional codes Measurement Computer vision Pose estimation Robot vision systems Data visualization Computer architecture

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Estimating Multiple Emotion Descriptors by Separating Description and Inference

Estimating Multiple Emotion Descriptors by Separating Descri...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Deng, Didan Shi, Bertram E. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To describe complex emotional states, psychologists have proposed multiple emotion descriptors: sparse descriptors like facial action units, continuous descriptors like valence and arousal, and discrete class descriptors like the expressions of happiness and anger. According to Cohn et al. [1], facial action units are sign vehicles that convey the emotion message, while discrete or continuous emotion descriptors are the messages perceived by observers. They differ in their focuses. Sign vehicles focus on describing facial behavior. Emotion messages focus on an observer's inference about the underlying state of the subject from facial behavior. We describe a novel architecture for multiple emotion descriptor estimation that incorporates this prior knowledge about the differences between descriptive labels (sign vehicles, like facial action units) and inferential labels (emotion messages like discrete emotion expressions, valence, and arousal). In our multi-level architecture, a common set of low-level features of facial regions are fed into two separate branches: one for descriptive labels and the other for inferential labels. The differences between these two branches reflects the differences between the two types of labels. Sign vehicles are typically more specific and spatially localized. Emotion messages are reflected across the entire face. Our experiments on the ABAW3 challenge [9] dataset demonstrate this approach outperforms all other submitted approaches to multi-task learning.

关键词： Space vehicles Representation learning visualization Emotion recognition Gold Face recognition Psychology

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Analyzing and Overcoming Degradation in Warm-Start Reinforcement Learning

Analyzing and Overcoming Degradation in Warm-Start Reinforce...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Wexler, Benjamin Sarafian, Elad Kraus, Sarit Bar Ilan Univ Dept Comp Sci Ramat Gan Israel

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Reinforcement Learning (RL) for robotic applications can benefit from a warm-start where the agent is initialized with a pretrained behavioral policy. However, when transitioning to RL updates, degradation in performance can occur, which may compromise the robot's safety. This degradation, which constitutes an inability to properly utilize the pretrained policy, is attributed to extrapolation error in the value function, a result of high values being assigned to Out-Of-Distribution actions not present in the behavioral policy's data. We investigate why the magnitude of degradation varies across policies and why the policy fails to quickly return to behavioral performance. We present visual confirmation of our analysis and draw comparisons to the Offline RL setting which suffers from similar difficulties. We propose a novel method, Confidence Constrained Learning (CCL) for Warm-Start RL, that reduces degradation by balancing between the policy gradient and constrained learning according to a confidence measure of the Q-values. For the constrained learning component we propose a novel objective, Positive Q-value Distance (CCL-PQD). We investigate a variety of constraint-based methods that aim to overcome the degradation, and find they constitute solutions for a multi-objective optimization problem between maximimal performance and miniminal degradation. Our results demonstrate that hyperparameter tuning for CCL-PQD produces solutions on the Pareto Front of this multi-objective problem, allowing the user to balance between performance and tolerable compromises to the robot's safety.

关键词： Degradation Measurement Extrapolation visualization Reinforcement learning Behavioral sciences Safety

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Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT’s Potential to Apply Graph Layout Algorithms

Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT...

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25th Eurographics conference on visualization, EuroVis 2023

作者： Di Bartolomeo, Sara Severi, Giorgio Schetinger, Victor Dunne, Cody Northeastern University United States TU Wien Austria

ISBN: (纸本)9783038682196

Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions—such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT, one of the most recent cutting-edge LLMs made available to the public. These algorithms are used to create readable graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs’ ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs’ capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs;a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing—using the Sugiyama algorithm as a sample case—while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials to reproduce our results is available on *** . © 2023 The Authors.

关键词： Data visualization

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SEC-UNet: Squeeze-and-Excitation Vision Transformer and Cross Attention Enhanced UNet for Electrical Impedance Tomography

SEC-UNet: Squeeze-and-Excitation Vision Transformer and Cros...

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ieee Instrumentation and Measurement Technology conference

作者： Zichen Wang Tao Zhang Qi Wang Ronghua Zhang School of Electronic and Information Engineering Tiangong University Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Electrical Impedance Tomography (EIT), recognized for its visualization and non-invasive characteristics, holds broad application prospects in industrial and biomedical fields. However, its inherent ill-posedness and nonlinearity in the inverse problem led to low spatial resolution, boundary distortion, and inaccurate conductivity representation in current imaging methods. This paper introduces an enhanced U-shaped deep imaging approach, referred to SEC-UNet, which optimizes skip connections using SE attention and Vision Transformer (ViT) to build global information features. Moreover, a cross-attention module is introduced for fusing multi-scale information between encoder and decoder. The experiments show that SEC-UNet achieves a Root Mean Square Error (RMSE) of 2.5067 and a Structural Similarity Index (SSIM) of 0.9282, improving RMSE by 31.01% and SSIM by 1.25% over state-of-the-art Transformer-based and ConvNets-based methods. The visualizations demonstrate better boundary preservation and consistency with actual distributions. The results confirm the enhanced robustness and generalizability, indicating the superiority of the Transformer framework in soft-field imaging over ConvNets.

关键词： visualization Electrical impedance tomography Shape Imaging Conductivity Transformers Robustness

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RiskRadar: an Online Mapping Platform for Disaster Preparedness and Response via Comprehensive Data Integration and visualization

RiskRadar: an Online Mapping Platform for Disaster Preparedn...

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2025 ieee Green Technologies conference, GreenTech 2025

作者： Kim, Sunghwan Kim, Juho Park, Junhyeong Ku, Seongyun You, Minji Whitaker, Mark Douglas School of Engineering Doerr School of Sustainability Stanford University & Caravan Research StanfordCA United States University of Ghent & Caravan Research Ghent Belgium Stony Brook University & Caravan Research Stony BrookNY United States Graduate School of Business Kyung Hee University Seoul Korea Republic of

ISBN: (纸本)9798331520182

Updatable and accurate mapping tools are essential for effective disaster management to safeguard public safety before, during, or after disasters. Maps help prioritize where assets are deployed in disasters in efficient ways and are important for compiling data layers about normal environmental risk inequalities of various indicators in general, to locate geographically the most vulnerable populations in order to improve their lives. Central to both kinds of mapping efforts are accessibility and clarity of information, whether in plotting a rapidly evolving natural or human-made hazard in a disaster zone, or for sharing with first responders, or for ongoing daily awareness and thus better administration about regular environmental risk in national populations. The RiskRadar platform prototype improves five shortcomings found regularly in other platforms for disaster risk reduction (DRR) and has more comprehensive data integration and visualization tools with its unique data indexes to enhance disaster preparedness and response. By integrating diverse environmental and urban data sets, RiskRadar shows different kinds of risk easily. From this, it facilitates informed decision-making on priorities and timely actions, while aiding more coordinated awareness and thus coordinated response to risk. This paper examines functionalities of a prototype platform for how it bolsters disaster resilience particularly in contexts desired by the United Nations Office for Disaster Risk Reduction (UNDRR). With a user-friendly interface, RiskRadar lets stakeholders, first responders, and the public at large access the same critical information. Good digitally-accessible maps aid better trust upon which better coordination for disaster management and higher group preparedness for future events can take place. © 2025 ieee.

关键词： visualization

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On the Building of a Common In-Situ visualization Environment for Arm A64FX Supercomputers

On the Building of a Common In-Situ Visualization Environmen...

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ieee International conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Jorji Nonaka Masahiro Nakao Hitoshi Murai Keiji Yamamoto Masaaki Terai Tomohiro Kawanabe Toshihiko Kai Fumiyoshi Shoji Daichi Obinata Hiroyuki Ito Shunji Uno Takanori Haga Manabu Motokawa Atsushi Fujino Naoyuki Fujita Seiji Tsutsumi Atsushi Toyoda Naohisa Sakamoto RIKEN Center for Computational Science (R-CCS) Japan Fujitsu Limited Japan Ryoyu Systems Japan Japan Aerospace Exploration Agency (JAXA) Japan Intelligent Light Japan Kobe University Japan

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

This poster presents a collaborative activity among R-CCS and JAXA towards the development of a common in-situ visualization infrastructure on the Arm A64FX supercomputers at both sites, which includes the Fugaku and Fujitsu FX700 on the R-CCS, and Fujitsu FX1000, named as TOKI-SORA, on the JAXA side. This large data visualization-related activity is just a part of a wide-ranging collaboration among the operational groups of the Japanese HPC sites running those Arm A64FX supercomputers. Although each HPC site has its own missions and targets, the visualization and analysis of extreme-scale simulation results still remain a common challenging problem, and thus, we have been evaluating the use of commercial and open-source software aiming to provide a useful in-situ visualization environment for the HPC users. In this poster, we will present the current development stage of this environment on the Fugaku.

关键词： visualization Costs conferences Simulation Buildings Data visualization Collaboration Cluster computing Supercomputers Open source software

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visualization of Comorbidities in Inflammatory Bowel Diseases through Networks

Visualization of Comorbidities in Inflammatory Bowel Disease...

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ieee International conference on Bioinformatics and Biomedicine (BIBM)

作者： Rosarina Vallelunga Marianna Milano Rocco Spagnuolo Lidia Giubilei Evelina Suraci Mario Cannataro Francesco Luzza Department of Health Sciences University of Catanzaro Italy Department of Experimental and Clinical Medicine University of Catanzaro Italy Department of Medical and Surgical Sciences University of Catanzaro Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Inflammatory Bowel Diseases (IBD), including Crohn’s Disease (CD) and Ulcerative Colitis (UC), are chronic conditions characterized by a complex network of comorbidities that significantly impact patients’ quality of *** this study, we employed a Network visualization approach to explore the comorbidities associated with IBD, using data extracted from the clinical records of patients at the Digestive Pathophysiology Unit of the University Hospital “Renato Dulbecco”.By constructing networks, we mapped and compared the comorbidities of Crohn’s Disease and Ulcerative Colitis, considering gender differences as well. The graphs produced offer a clear view clearly shows the major comorbidities and their distribution, highlighting significant differences between the two diseases and across *** visual approach facilitates a better understanding of the complexity of clinical interactions in IBD, suggesting new perspectives for personalized and multidisciplinary therapeutic approaches in managing these diseases.

关键词： visualization Hospitals Data visualization Complex networks Complexity theory Data mining Bioinformatics Diseases Ulcerative colitis

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