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IEEE MULTIMEDIA 1995年第1期2卷 94-99页

作者： KELLY, P MOEZZI, S Visual Computing Laboratory

It would be difficult to overestimate the importance of visual information in current computer systems. visual computing, which embraces processing, interpreting, modeling, assimilating, storing, retrieving, and synthesizing visual information, now plays a crucial role in many fields. These include multimedia, virtual reality, robotics, scientific visualization, and communications systems. And the demand for further integration of visual information into these areas shows every sign of continuing unabated. Under the direction of Ramesh Jain, the visual computing Laboratory at the University of California, San Diego, was established as a center for innovative visual computing research to address the requirements of these applications in next-generation computer technologies. As such, the visual computing Lab hosts a group of researchers working in a variety of areas, notably multimedia databases, information assimilation, interactive video, and visual interaction through gesture recognition. This article presents a high-level overview of activities in the visual computing Laboratory and provides some details on prototype systems that we are currently developing.< >

关键词： Laboratories Information retrieval Multimedia systems Virtual reality Robots visualization Application software Computer applications Multimedia databases Prototypes

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Contemporary visual computing: A system perspective

Contemporary visual computing: A system perspective

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Chen, Chang-Wen Chair Professor of Visual Computing Department of Computing The Hong Kong Polytechnic University Hong Kong

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Special issue — visual computing and communications

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Canadian Journal of Electrical and Computer Engineering 1998年第1-2期23卷 3-4页

作者： Sethuraman Panchanathan Visual Computing and Communications Laboratory Department of Computer Science and Engineering Arizona State University Tempe Arizona USA 85287-5406

Le traitement de l'information visuelle en communications devient de plus en plus important avec le développement des réseaux à large bande, de la technologie de l'image et du vidéo, des stations de travail et processeurs multimédia de haut de gamme, et des outils pour l'indexation, le furetage et l'archivage.

关键词： Multimedia communication visualization Streaming media Image coding Special issues and sections Transform coding

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Data Science and visual computing 1

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丛书名： Advanced Information and Knowledge Processing

1000年

作者： Rae Earnshaw John Dill David Kasik

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Mobile Cloud visual Media computing 1

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1000年

作者： Gang Hua Xian-Sheng Hua

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Explicit exponential rosenbrock methods and their application in visual computing

arXiv

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arXiv 2018年

作者： Luan, Vu Thai Michels, Dominik L. Department of Mathematics Southern Methodist University PO Box 750156 DallasTX75275-0156 United States Computational Sciences Group Visual Computing Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

We introduce a class of explicit exponential Rosenbrock methods for the time integration of large systems of stiff differential equations. Their application with respect to simulation tasks in the field of visual computing is discussed where these time integrators have shown to be very competitive compared to standard techniques. In particular, we address the simulation of elastic and nonelastic deformations as well as collision scenarios focusing on relevant aspects like stability and energy conservation, large stiffnesses, high fidelity and visual accuracy.65L04, 74S30, 74B15, 74B20, 74H05, 74H15, 74H40 Copyright © 2018, The Authors. All rights reserved.

关键词： Differential equations

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Explainable XR: Understanding User Behaviors of XR Environments Using LLM-Assisted Analytics Framework

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IEEE Transactions on visualization and Computer Graphics 2025年第5期31卷 2756-2766页

作者： Kim, Yoonsang Aamir, Zainab Singh, Mithilesh Boorboor, Saeed Mueller, Klaus Kaufman, Arie E. Center for Visual Computing Stony Brook University New York United States

We present Explainable XR, an end-to-end framework for analyzing user behavior in diverse eXtended Reality (XR) environments by leveraging Large Language Models (LLMs) for data interpretation assistance. Existing XR user analytics frameworks face challenges in handling cross-virtuality - AR, VR, MR - transitions, multi-user collaborative application scenarios, and the complexity of multimodal data. Explainable XR addresses these challenges by providing a virtuality-agnostic solution for the collection, analysis, and visualization of immersive sessions. We propose three main components in our framework: (1) A novel user data recording schema, called User Action Descriptor (UAD), that can capture the users' multimodal actions, along with their intents and the contexts;(2) a platform-agnostic XR session recorder, and (3) a visual analytics interface that offers LLM-assisted insights tailored to the analysts' perspectives, facilitating the exploration and analysis of the recorded XR session data. We demonstrate the versatility of Explainable XR by demonstrating five use-case scenarios, in both individual and collaborative XR applications across virtualities. Our technical evaluation and user studies show that Explainable XR provides a highly usable analytics solution for understanding user actions and delivering multifaceted, actionable insights into user behaviors in immersive environments. © 1995-2012 IEEE.

关键词： visual analytics

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Is facial beauty in the eyes? A multi-method approach to interpreting facial beauty prediction in machine learning models

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Discover Artificial Intelligence 2025年第1期5卷 1-18页

作者： Ibrahim, Ahmed Aman Ugail, Noah Hassan Ugail, Hassan Centre for Visual Computing and Intelligent Systems University of Bradford Bradford United Kingdom Department of Mathematical Sciences University of Bath Bath United Kingdom

Despite advances in facial beauty prediction, how specific facial regions contribute to perceptions of attractiveness remains largely unexplored, highlighting a critical interpretability gap in this domain. This study addresses the interpretability gap in facial beauty prediction (FBP) models by introducing a novel framework that combines global and local interpretability methods. We introduce Region Attribution, a technique that aggregates XRAI (eXplanation with Ranked Area Integrals) saliency maps across predefined facial regions to quantify their relative importance in individual predictions. Two global approaches complement this local interpretability: permutation feature importance, which systematically explores individual facial regions across the dataset to measure performance degradation, and individual feature prediction, where separate CNN models are trained on isolated facial regions to assess their independent predictive power. Using the SCUT-FBP5500 and MEBeauty datasets, we train convolutional neural networks on both full faces and individual facial features. While our findings reveal slight variations in feature rankings across the three methods, they consistently identify the eyes and nose regions as crucial determinants in facial beauty prediction. Thus, this study demonstrates the value of a multi-method approach in understanding the complex interplay of facial features in beauty prediction machine learning models. © The Author(s) 2025.

关键词： Prediction models

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Silo: Half-Gigapixel Cylindrical Stereoscopic Immersive Display 32

Silo: Half-Gigapixel Cylindrical Stereoscopic Immersive Disp...

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32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Boorboor, Saeed Gutierrez-Rosales, Doris Shoaib, Ahamed Kalsi, Chahat Wang, Yue Cao, Yuyang Gu, Xianfeng Kaufman, Arie E. Stony Brook University Center for Visual Computing Stony BrookNY United States

ISBN: (纸本)9798331536459

We present the design and construction of the Silo, a fully immersive stereoscopic cylindrical tiled-display visualization facility. Comprising 168 high-density LCD displays, the facility provides an ultra-high-resolution image of 619 million pixels, and close to 360 horizontal field-of-regards (FoR), aiming to maximize visual acuity and completely engage the human visual sensorium and its periphery. In this article, we outline the motivations, design principles, hardware selection and software systems, and interaction modalities used in constructing the Silo. To address missing visual information due to the absence of a ceiling and floor, we have designed a method that utilizes conformal mapping and optimal mass transport to reproject the entire 360 volumetric FoR of the virtual scene to the available display real estate. We showcase several applications demonstrating the utility of the Silo and report the findings of our user studies that highlight the effectiveness of the Silo layout compared to curved mono and flat powerwall display facilities. Our user evaluations and studies have shown that the Silo supports natural exploration and enhanced visualization due to its capability to render surround ultra-high-resolution stereoscopic views. © 2025 IEEE.

关键词： Conformal mapping

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Sequential Fusion of Text-close and Text-far Representations for Multimodal Sentiment Analysis 31

Sequential Fusion of Text-close and Text-far Representations...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sun, Kaiwei Tian, Mi Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798891761964

Multimodal Sentiment Analysis (MSA) aims to identify human attitudes from diverse modalities such as visual, audio and text modalities. Recent studies suggest that the text modality tends to be the most effective, which has encouraged models to consider text as its core modality. However, previous methods primarily concentrate on projecting modalities other than text into a space close to the text modality and learning an identical representation, which does not fully make use of the auxiliary information provided by audio and visual modalities. In this paper, we propose a framework, Sequential Fusion of Text-close and Text-far Representations (SFTTR), aiming to refine multimodal representations from multimodal data which should contain both representations close to and far from the text modality. Specifically, we employ contrastive learning to sufficiently explore the information similarities and differences between text and audio/visual modalities. Moreover, to fuse the extracted representations more effectively, we design a sequential cross-modal encoder to sequentially fuse representations that are close to and far from the text modality. Experiments on three public benchmark datasets, MOSI, MOSEI, and CH-SIMS, demonstrate the superiority of the proposed method over the state-of-the-arts. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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