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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10259 条记录，以下是181-190 订阅

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CoMoFusion: Fast and high-Quality Fusion of Infrared and Visible Image with Consistency Model 7th

CoMoFusion: Fast and High-Quality Fusion of Infrared and Vis...

引用

7th Chinese conference on Pattern Recognition and computer Vision

作者： Meng, Zhiming Li, Hui Zhang, Zeyang Shen, Zhongwei Yu, Yunlong Song, Xiaoning Wu, Xiaojun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Int Joint Lab Artificial Intelligence Jiangsu Pro Wuxi 214122 Peoples R China Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9789819786848;9789819786855

Generative models are widely utilized to model the distribution of fused images in the field of infrared and visible image fusion. However, current generative models based fusion methods often suffer from unstable training and slow inference speed. To tackle this problem, a novel fusion method based on consistency model is proposed, termed as CoMoFusion, which can generate high-quality images and achieve fast image inference speed. In specific, consistency model is used to construct multi-modal joint features in the latent space with the forward and reverse process. Then, the infrared and visible features extracted by the trained consistency model are fed into fusion module to generate the final fused image. In order to enhance the texture and salient information of fused images, a novel loss based on pixel value selection is also designed. Extensive experiments on public datasets illustrate that our method obtains the SOTA fusion performance compared with the existing fusion methods. The code is available at https://***/ZhimingMeng/CoMoFusion.

关键词： Image fusion Multi-modal information Consistency model Diffusion

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Image Denoising with CNN-Based Attention 4

Image Denoising with CNN-Based Attention

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4th International Informatics and Software Engineering conference, IISEC 2023

作者： Karaca, Neval Ciftci, Serdar Harran University Department of Computer Engineering Şanlıurfa Turkey

ISBN: (纸本)9798350318036

Noise removal is one of the most commonly used processes in computer vision. Noise removal improves the quality of the image, thereby improving the performance of computer vision algorithms and providing user pleasing. In this study, we aim to improve the performance of noise removal by adding an efficient attention module, the Convolutional Block Attention Module (CBAM), to the Fast and Flexible Denoising Network (FFDNet) model with an adjustable noise level map as input. By adding the CBAM module to the convolutions used in FFDNet, the CNN's representational power was increased and successful results were obtained. The proposed method achieved high PSNRs in quantitative experiments on different datasets, and in qualitative experiments we observed that the denoised images are close to the target images. © 2023 IEEE.

关键词： Image enhancement

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Comparison of Learning Achievement with Interactive Digital Lessons on Using a program for Basic Photo Editing: Case Study of Daruna Vocational Certificate Students

Comparison of Learning Achievement with Interactive Digital ...

引用

2024 IEEE International conference on Cybernetics and Innovations, ICCI 2024

作者： Narin, Burin Nimnual, Jirayu Srisakonsub, Phatthranit Muban Chombueng Rajabhat University Department Of Computer Education Ratchaburi Thailand

ISBN: (纸本)9798350370096

This paper presents experimental research aimed at comparing the academic achievement of students who have studied with an interactive digital lesson on using basic image editing programs designed for vocational certificate students. Additionally, the study seeks to assess students' satisfaction with these lessons. The research population and sample consist of students enrolled in the computer Graphics course during Semester 1/2023 at Daruna Ratchaburi Vocational College, selected through purposive sampling. The research utilized the following tools: 1) an interactive digital lesson on using a program for basic image editing for Vocational Certificate students 2) an assessment form for evaluating the quality of interactive digital lessons, 3) an academic achievement test and 4) an assessment of student satisfaction with the interactive digital lessons. The results of the research indicate that the academic achievement of students who participated in the interactive digital lessons on the use of basic image editing programs for vocational certificate students improved significantly compared to their performance before studying, with statistical significance at the 0.05 level. Moreover, the satisfaction level of students with the digital lessons was found to be high (x = 4.54, S.D. = 0.34) © 2024 IEEE.

关键词： Students

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Low-Cost, high-performance Algorithm and Hardware Architecture for SAO Mode Decision 2

Low-Cost, High-Performance Algorithm and Hardware Architectu...

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2nd IEEE International conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Hou, Bingjing Liu, Chang Zhang, Yuejun Huang, Leilei Jing, Minge Fan, Yibo Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China East China Normal University Institute of Microelectronic Circuits and Systems Shanghai China

ISBN: (纸本)9798331515669

Sample adaptive offset (SAO) is applied for reducing sample distortion and attenuating ringing artifacts in both HEVC and VVC standards. The rate-distortion optimization process is used to select the best SAO parameter. It accounts for the majority of SAO complexity and determines the SAO filtering performance. Therefore, the rate-distortion cost calculation should be well handled to achieve both low cost and high coding performance. In this paper, we propose a simplified and optimized rate estimation algorithm based on the characteristics of SAO parameter syntax and entropy coding process. And we propose a hierarchical flow for rate-distortion cost calculation to further reduce the computational cost and storage cost for SAO mode decision hardware. The results show that the proposed algorithm achieves an average YUV loss of -0.10%, and the proposed SAO mode decision hardware architecture can be implemented by only 21.8k gates using TSMC 65nm process under 400 MHz. © 2024 IEEE.

关键词： Electric distortion

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UpDown: Combining Scalable Address Translation with Locality Control

UpDown: Combining Scalable Address Translation with Locality...

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2024 Workshops of the International conference for high performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Wang, Yuqing Perarnau, Swann Chien, Andrew A. University of Chicago Department of Computer Science Chicago United States Argonne National Laboratory Chicago United States

ISBN: (纸本)9798350355543

Global shared memories of petabytes are increasingly useful for applications, but traditional page-based techniques do not scale (limited reach), and scalable techniques such as segments fail to provide needed locality control. We propose a novel two-level translation scheme, UpDown, that provides compact, efficient access control to distributed segments of arbitrary size and data layout control, solving problems of reach and data locality. UpDown's novel two-level structure separates access control from data layout, allowing applications to manage locality cheaply, without privileged *** evaluate UpDown against page-based systems, using big data computations. Our results show that UpDown is scalable and provides effective global data locality management. UpDown's translation states are an average of ~620 billion times smaller total and ~130 billion times smaller local translation state. Simulations with synthetic traces show that the two-level translation scheme enables fast, user-level management of data locality in a scalable parallel machine. © 2024 IEEE.

关键词： address translation global address space page translation parallel machine process isolation segment translation virtual memory

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Discriminative Dimension Selection for Enhancing the Interpretability and performance of Clustering Output 16

Discriminative Dimension Selection for Enhancing the Interpr...

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16th International conference on Knowledge and Smart Technology, KST 2024

作者： Lian, Hrang Kap Waiyamai, Kitsana Konstantinos, Slavakis Haruechaiyasak, Choochart Kasetsart University Department of Electrical Engineering Bangkok Thailand Kasetsart University Department of Computer Engineering Bangkok Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Tokyo Japan Artificial Intelligence Research Group National Electronics and Computer Technology Center Bangkok Thailand

ISBN: (纸本)9798350370737

Discriminative Dimension Selection (DDS) has emerged as a powerful tool for identifying the most relevant features in high-dimensional datasets, enabling interpretable data analysis and visualization. This paper explores the application of DDS which utilizes overlapping clusters and dimensions to enhance the interpretability and performance of the K-means clustering algorithm. Our approach leverages the post-processing capabilities of K-means to selectively retain informative features and discard redundant or irrelevant ones. This refined feature set not only preserves the clustering performance of K-means but also enhances its interpretability and visualization. We demonstrate the effectiveness of our method using a variety of datasets and compare its performance against traditional K-means and the proposed method Overlap-resolved Clustering (ORC). We also test with multiple cluster validity indices such as the Silhouette Coefficient Score (SS), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). Our results consistently show that ORC produces better clustering results and enhances the interpretability of the datasets. This study highlights the potential of DDS as a valuable tool for improving the interpretability and visualization of high-dimensional data analysis using K-means clustering. © 2024 IEEE.

关键词： Visualization

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LSGraph: A Locality-centric high-performance Streaming Graph Engine 24

LSGraph: A Locality-centric High-performance Streaming Graph...

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19th European conference on computer Systems, EuroSys 2024

作者： Qi, Hao Wu, Yiyang He, Ligang Zhang, Yu Luo, Kang Cai, Minzhi Jin, Hai Zhang, Zhan Zhao, Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom Zhejiang Lab China

ISBN: (纸本)9798400704376

Streaming graph has been broadly employed across various application domains. It involves updating edges to the graph and then performing analytics on the updated graph. However, existing solutions either suffer from poor data locality and high computation complexity for streaming graph analytics, or need high overhead to search and move graph data to ensure ordered neighbors during streaming graph update. This paper presents a novel locality-centric streaming graph engine, called LSGraph, to enable efficient both graph analytics and graph update. The main novelty of this engine is a differentiated hierarchical indexed streaming graph representation approach to achieve efficient data search and movement for graph update and also maintain data locality and ordered neighbors for efficient graph analytics simultaneously. Besides, a locality-aware streaming graph data update mechanism is also proposed to efficiently regulate the distance of data movement, minimizing the overhead of memory access during graph update. We have implemented LSGraph and conducted a systematic evaluation on both real-world and synthetic datasets. Compared with three cutting-edge streaming graph engines, i.e., Terrace, Aspen, and PaC-tree, LSGraph achieves 2.98×-81.08×, 1.46×-12.56×, and 1.26×-10.31× speedups during graph update, while obtaining 1.02×-4.28×, 1.58×-3.55×, and 1.20×-2.72× speedups during graph analytics, respectively. © 2024 Owner/Author.

关键词： Engines

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Exploiting GPU computing for effective Agent-Based simulation: initial experiments 33

Exploiting GPU computing for effective Agent-Based simulatio...

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

作者： Pennisi, Marzio Franceschinis, Giuliana Baccega, Daniele Pernice, Simone Terrone, Irene Computer Science Institute - DiSIT Università del Piemonte Orientale Alessandria Italy University of Turin Turin Department of Computer Science Italy

ISBN: (纸本)9798331524937

Agent-Based Modeling and Simulation (ABMS) has been increasingly applied in various research fields, thanks to the capability of these models to describe fine-grained realworld behavior and to the ease of interpretation by domain experts. However, such models lack a formal definition and well-defined semantics that are common to the different tools supporting ABMS. This may occasionally lead to greater complexity in interpreting the results with respect to other modeling approaches. To address this issue, an ABM semantics that adopts a continuous-time approach and a next-event time advance simulation algorithm has been formally defined and presented. Such an approach may lead to high computation times as it requires recalculations of activity rates for all the agents after each event. In this preliminary study, we exploit the FLAME GPU framework to evaluate the benefits that GPU computing may bring to the performance of our simulation algorithm. © 2025 IEEE.

关键词： program processors

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Frustum PointVoxel-RCNN: A high-performance Framework for Accurate 3D Object Detection in Point Clouds and Images 4

Frustum PointVoxel-RCNN: A High-Performance Framework for Ac...

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4th International conference on computer, Control and Robotics (ICCCR)

作者： Shao, Shilin Zhou, Yang Li, Zhenglin Xu, Wentai Chen, Guangtao Yuan, Tianxin Shanghai Univ Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Mechatron Engn & Automat Shanghai Peoples R China Shanghai Univ Future Technol Shanghai Peoples R China

ISBN: (纸本)9798350373141;9798350373158

Precise 3D object detection plays a pivotal role within the perception module of autonomous vehicles. Many approaches have shown promising results for 3D object detection with lidar. Voxel-based methods are computationally efficient but suffer from information loss, while point-based methods handle irregular data but require extensive computations and optimization processes. In this paper, a high-performance 3D object detection framework named Frustum PointVoxel-RCNN has been developed. Using 2D object detection with frustums effectively mitigates the presence of irrelevant point clouds associated with the objects. Afterwards, 3D Voxel CNN and PointNet-based set abstraction are used to learn the features of the frustum-processed point clouds. Our method significantly reduces the computation cost of frustum-processed point clouds in 3D Voxel CNN and PointNet-based networks. Extensive experiments on the KITTI dataset shown our proposed method surpassed stae-of-the-art 3D detection methods when accurate 2D detections are precise. Specifically, it yielded significant speed advantages and improved accuracy when the scene was at a hard level. Our proposed method achieves a 50% improvement in computational efficiency compared to PV-RCNN.

关键词： Frustum PV-RCNN 3D object detection

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Analysis of MFCC and Chromagram Features in Lightweight Bidirectional LSTM and Attention Models for Music Genre Classification 21

Analysis of MFCC and Chromagram Features in Lightweight Bidi...

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21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Huang, Renxiang Li, Xu Li, Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China Khoury College of Computer Science Northeastern university BostonMA02115 United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798331519254

Music genre classification is essential for organizing music libraries and enhancing recommendation systems. This paper evaluates four lightweight models combining Mel Frequency Cepstral Coefficients (MFCCs) and Chromagram features with Bidirectional LSTM and Multi-head Attention architectures. Using the GTZAN dataset, we found that models with Multi-head Attention, outperformed with MFCCs features, achieving a test accuracy of 85.6%. MFCCs excel in capturing timbral characteristics critical for genre classification, while Chromagram features proved less effective. Although overfitting occurred, future studies can focus on incorporating methods like data augmentation and regularization to improve performance. These lightweight models offer a balance between accuracy and computational efficiency, making them suitable for real-time applications. © 2024 IEEE.

关键词： Real time systems

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