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

共 54092 条记录，以下是361-370 订阅

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Enhancing Visuo-Haptic Coherency by Manipulating Fingertip Contact Tilt

Enhancing Visuo-Haptic Coherency by Manipulating Fingertip C...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Hiroki Ota Yutaro Hirao Monica Perusquía-Hernández Hideaki Uchiyama Kiyoshi Kiyokawa Maud Marchal Nara Institute of Science and Technology University de Rennes INSA and IRISA

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This study investigates the manipulation of fingertip contact plane manipulation to enhance visuo-haptic coherency of virtual object. We designed a compact device that adjusts fingertip contact plane tilt to simulate diverse 3D shapes. Through user studies, the device demonstrated significant improvements in haptic realism, enjoyment, and operability compared to conventional methods. These findings highlight the potential of fingertip contact plane manipulation as a novel approach for advancing virtual reality (VR) haptic interfaces, enabling more immersive and realistic experiences.

关键词： Training visualization Solid modeling Three-dimensional displays Shape Refining Tactile sensors Virtual reality User interfaces Virtual prototyping

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Glimpse of MCQ Based VQA in Road & Traffic Scenarios

Glimpse of MCQ Based VQA in Road & Traffic Scenarios

引用

ieee Winter Applications and Computer Vision Workshops (WACVW)

作者： Athira Krishnan R Sumukha BG Ambarish Parthasarathy Indian Institute of Technology Hyderabad

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Multi-modal models have been a boon to the commu-nity over the last decade. The Large Language and Vision Models for Autonomous Driving (LLVM-AD) challenge [3] is a Visual Question Answering (VQA) problem in Multiple Choice Question (MCQ) answering in road/traffic scenarios. We have attempted this as a supervised clas-sification task. In this exercise, we have explored vari-ous models, from transformer-based multi-modal models to recent Vision Language Models (VLMs). Even though we have experimented with time and compute constraints, the results show that the models could enhance the ca-pability if trained for longer iterations. We have also compared the model sizes considering the limited tar-get edge compute. Codes are available on the following https://***/Athirakr94/***.

关键词： Analytical models visualization Computer vision Computational modeling Roads conferences Transformers Question answering (information retrieval) Time factors Load modeling

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Recognizing Unseen States of Unknown Objects by Leveraging Knowledge Graphs

Recognizing Unseen States of Unknown Objects by Leveraging K...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Filippos Gouidis Konstantinos Papoutsakis Theodore Patkos Antonis Argyros Dimitris Plexousakis Foundation for Research and Technology-Hellas Greece University of Crete Greece Hellenic Mediterranean University Greece

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We investigate the problem of Object State Classification (OSC) in the context of zero-shot learning. Specifically, we propose the first method for Zero-shot Object-agnostic State Classification (OaSC) that, given an image, infers the state of a single object without relying on the knowledge or the estimation of the object class. In that direction, we capitalize on Knowledge Graphs (KGs) for structuring and organizing external knowledge, which, in combination with visual information, enable effective inference of the states of objects that have not been encountered in the training set. Having this unique property, a significant strength of our method is that it can handle an Open Set of object classes. We investigate the performance of OaSC in various datasets and settings, against several hypotheses and in comparison with state-of-the-art approaches for object attribute classification. OaSC outperforms these methods significantly across all benchmarks. 1

关键词： Training visualization Computer vision Zero shot learning Estimation Knowledge graphs Benchmark testing Graph neural networks

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High-Pass Kernel Prediction for Efficient Video Deblurring

High-Pass Kernel Prediction for Efficient Video Deblurring

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Bo Ji Angela Yao National University of Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

State-of-the-art video deblurring methods use deep network architectures to recover sharpened video frames. Blurring especially degrades high-frequency (HF) information, yet this aspect is often overlooked by recent models that focus more on enhancing architectural design. Recovering these fine details is challenging, partly due to the spectral bias of neural networks, which are inclined towards learning low-frequency functions. To address this, we enforce explicit network structures to capture the fine details and edges. We dynamically predict adaptive high-pass kernels from a linear combination of high-pass basis kernels to extract high-frequency features. This strategy is highly efficient, resulting in low-memory footprints for training and fast run times for inference, all while achieving state-of-the-art when compared to low-budget models. The code is available at https://***/jibo27/AHFNet.

关键词： Training Maximum likelihood detection Adaptation models visualization Computational modeling Nonlinear filters Feature extraction Prediction algorithms High frequency Kernel

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A Performance Model of In-Situ Techniques

A Performance Model of In-Situ Techniques

引用

33rd Euromicro International conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Ju, Yi Vidal, Nicolas Perez, Adalberto Gainaru, Ana Suter, Fred Markidis, Stefano Schlatter, Philipp Klaskyll, Scott Laure, Erwin Max Planck Computing and Data Facility Germany Oak Ridge National Laboratory United States KTH Royal Institute of Technology Sweden Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

ISBN: (纸本)9798331524937

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications. © 2025 ieee.

关键词： Data visualization

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Semantic Clustering of Image Retrieval Databases used for Visual Localization

Semantic Clustering of Image Retrieval Databases used for Vi...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Henry Hölzemann Torsten Fiolka Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Accurate self-localization of unmanned aerial systems (UAS) is needed to reduce their dependency on global navi-gation satellite systems (GNSS). Image retrieval techniques comparing aerial images with a reference database can be used for visual localization (VL). But the search space may be vast and a full search not feasible on a small UAS. In this work, we propose a novel solution that divides the reference database into smaller clusters based on the semantic con-tent of images. To this end, we generate and make use of a dataset for semantic segmentation of aerial image captures. By characterizing scenes and objects in images semanti-cally, retrieval-based systems are able to differentiate images and scenes efficiently. Using a divide-and-conquer approach, images with similar semantics are matched within smaller partial databases. This technique leads to reduced search times and approaches VL as a feasible solution for UAS localization in large-scale outdoor environments.

关键词： Location awareness Meters visualization Satellites Semantic segmentation Semantics Image retrieval Image capture Visual databases Standards

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Spatial Frequency Interleaving Residual Autoencoder for Indoor Radio Map Reconstruction

Spatial Frequency Interleaving Residual Autoencoder for Indo...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shitong Chai Jiahui Li Mengyao Ma Junwen Xie Xiaopeng Fan Xianqi Zhang Faculty of Computing Harbin Institute of Technology Harbin China Wireless Technology Lab Huawei China Faculty of Computing Harbin Institute of Technology HIT Suzhou Research Institute Peng Cheng Laboratory Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Indoor radio maps with frequency domain data are difficult to reconstruct when only limited measurements at a few locations are available. Naive convolutional neural networks suffer from flawed structures in the frequency domain when predicting these radio maps, resulting in overly smoothed predictions. We propose a Spatial Frequency Interleaving Residual Autoencoder (SFIRA) architecture to tackle this problem, along with a Procedural Radio Map Generation (PRMG) method to address the data deficiency of indoor radio maps. Experimental results indicate that the proposed architecture achieves lower Normalized Root Mean Square Error (NRMSE). visualization of the reconstruction further suggests that the proposed methods are effective.

关键词： Deep learning Indoor radio communication Frequency-domain analysis Autoencoders Signal processing algorithms Data visualization Feature extraction Convolutional neural networks Speech processing Root mean square

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Method for Generating Three-Dimensional Digital Paper with Physical Properties and Paper-Pen Interaction

Method for Generating Three-Dimensional Digital Paper with P...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee International conference on

作者： Weitao Sun Zihui Wang Jinwei Li Zhongcheng Wu Hefei Institutes of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China High Magnetic Field Laboratory Hefei Institute of Physical Science Chinese Academy of Sciences Hefei China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the trend of digitalization of paper related fields, the ways to excavate values of traditional paper's physical properties for potential methodologies of document storage and visualization have become a concern for researchers. This article develops a method for simulating three-dimensional paper fiber network structure and paper-pen interaction based on the physical properties of the structure generated. The three-dimensional paper fiber network structure is simulated and the physical attributes are extracted based on KCL-PAKKA model and cellular automation. The method for paper-pen interaction includes functions like ink diffusion, stroke width control, handwriting smoothing and filling using second-order Bézier curves. The experimental results show the effectiveness of this method in 3D paper simulation and paper-pen interaction.

关键词： visualization Solid modeling Three-dimensional displays Smoothing methods Ink Optical fiber networks Market research Excavation Filling Consumer electronics

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Improving the Perturbation-Based Explanation of Deepfake Detectors Through the Use of Adversarially-Generated Samples

Improving the Perturbation-Based Explanation of Deepfake Det...

引用

ieee Winter Applications and Computer Vision Workshops (WACVW)

作者： Konstantinos Tsigos Evlampios Apostolidis Vasileios Mezaris Information Technologies Institute - CERTH Thessaloniki Greece

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

In this paper, we introduce the idea of using adversarially-generated samples of the input images that were classified as deepfakes by a detector, to form perturbation masks for inferring the importance of different input features and produce visual explanations. We generate these samples based on Natural Evolution Strategies, aiming to flip the original deepfake detector's decision and classify these samples as real. We apply this idea to four perturbation-based explanation methods (LIME, SHAP, SOBOL and RISE) and evaluate the performance of the resulting modified methods using a SOTA deepfake detection model, a benchmarking dataset (FaceForensics++) and a corresponding explanation evaluation framework. Our quantitative assessments document the mostly positive contribution of the proposed perturbation approach in the performance of explanation methods. Our qualitative analysis shows the capacity of the modified explanation methods to demarcate the manipulated image regions more accurately, and thus to provide more useful explanations.

关键词： Deepfakes visualization Computer vision Perturbation methods conferences Europe Detectors Production Benchmark testing Feature extraction

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SentioScape: Facilitating Effective Cross-Cultural Communication through Emotional Mixed Reality

SentioScape: Facilitating Effective Cross-Cultural Communica...

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2025 ieee conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Brophy, Jack Chen, Dunya Donna Person, Tanner Minamizawa, Kouta Barbareschi, Giulia Keio University Graduate School of Media Design Japan

ISBN: (纸本)9798331514846

Increasing global migration has led to the rapid growth of interactions between organizations, groups, and individuals from various cultures. As such, knowing how to decode communication involving conversational partners from other cultures is essential in avoiding misunderstandings and needless conflicts. We present SentioScape, a Mixed Reality (MR) system for aiding emotional expression and understanding to foster more effective cross-cultural communication. The system allows co-located users to customize their own abstract visual representations of their emotions and perceive those of another person. 20 participants took part in an experiment to evaluate the system, with preliminary results revealing some positive trends that suggest the system has potential as a tool for creating more effective emotional communication between high- and low-context communicators. © 2025 ieee.

关键词： cross-cultural communication emotion visualization mixed reality XR

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