检索结果-内蒙古大学图书馆

32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Huiming Gao, Wei Yu, Zhuozhen Zhao, Tiesong Li, Ge School of Electronic and Computer Engineering Shenzhen Graduate School Peng Cheng Laboratory Peking University Shenzhen China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China

ISBN: (纸本)9798400706868

With the rise of immersive media applications such as digital museums, virtual reality, and interactive exhibitions, point clouds, as a three-dimensional data storage format, have gained increasingly widespread attention. The massive data volume of point clouds imposes extremely high requirements on transmission bandwidth in the above applications, gradually becoming a bottleneck for immersive media applications. Although existing learning-based point cloud compression methods have achieved specific successes in compression efficiency by mining the spatial redundancy of their local structural features, these methods often overlook the intrinsic connections between point cloud data and other modality data (such as image modality), thereby limiting further improvements in compression efficiency. To address the limitation, we innovatively propose a view-guided learned point cloud geometry compression scheme, namely ViewPCGC. We adopt a novel self-attention mechanism and cross-modality attention mechanism based on sparse convolution to align the modality features of the point cloud and the view image, removing view redundancy through Modality Redundancy Removal Module (MRRM). Simultaneously, side information of the view image is introduced into the Conditional Checkboard Entropy Model (CCEM), significantly enhancing the accuracy of the probability density function estimation for point cloud geometry. In addition, we design a View-Guided Quality Enhancement Module (VG-QEM) in the decoder, utilizing the contour information of the point cloud in the view image to supplement reconstruction details. The superior experimental performance demonstrates the effectiveness of our method. Compared to the state-of-the-art point cloud geometry compression methods, ViewPCGC exhibits an average performance gain exceeding 10% on D1-PSNR metric. © 2024 ACM.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

MSFEN-AM: A Non-intrusive Load Identification Method for Power Saving 11

MSFEN-AM: A Non-intrusive Load Identification Method for Pow...

引用

2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Xu, Yiwen Liu, Dengfeng Lin, Zhiquan Zhao, Tiesong He, Nian Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China Fuzhou University Zhicheng College Fuzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350314014

In order to conserve power resources, researchers have been increasing their focus on load monitoring and identification technologies. However, current image classification methods for load identification face difficulties in extracting overall image contour features and complementing them based on image detail textures. To address this problem, we propose the MSFEN-AM network model, which consists of five parts: input part, downsampling module, bottlenick, upsampling module, and output part. We design a multi-scale feature dense extraction module in the network for feature reuse to improve information utilization. Additionally, we incorporated an attention mechanism to focus on extracting texture and edge information from the image. Furthermore, we introduced an attention gate mechanism to strengthen network selective features and suppress invalid information. Finally, we conducted experiments on the PLAID dataset and performed ablation tests on the multi-scale feature extraction modules and attention gating mechanism. The results show the superiority of our method. © 2023 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave Systems 24

Beam Scanning for Integrated Sensing and Communication in IR...

引用

24th IEEE International Workshop on Signal processing Advances in Wireless Communications, SPAWC 2023

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781665496261

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. © 2023 IEEE.

关键词： Millimeter waves

来源：评论

学校读者我要写书评

暂无评论

MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model

arXiv

引用

arXiv 2024年

作者： Wu, Tong Chen, Zhiyong Tao, Meixia Sun, Yaping Xu, Xiaodong Zhang, Wenjun Zhang, Ping Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Digital Media Processing and Transmission China Department of Broadband Communication Pengcheng Laboratory Shenzhen518055 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves state-of-the-art performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay. © 2024, CC BY.

关键词： Channel coding

来源：评论

学校读者我要写书评

暂无评论

MSFEN-AM: A Non-intrusive Load Identification Method for Power Saving

MSFEN-AM: A Non-intrusive Load Identification Method for Pow...

引用

International Conference on Information, Communication and Networks (ICICN)

作者： Yiwen Xu Dengfeng Liu Zhiquan Lin Tiesong Zhao Nian He Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Zhicheng College Fuzhou University Fuzhou China Peng Cheng Laboratory Shenzhen China

关键词：

来源：评论

学校读者我要写书评

暂无评论

RDVC: Efficient Deep Video Compression with Regulable Rate and Complexity Optimization

引用

IEEE Transactions on Multimedia 2025年

作者： Wei, Xiaojie Lin, Jielian Xu, Jiawei Gao, Wei Zhao, Tiesong Putian University School of Mechanical Electrical & Information Engineering PutianFujian China Shenzhen Graduate School Peking University School of Electronic and Computer Engineering Shenzhen518055 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Deep video coding has paved a way to break through the performance bottleneck of reigning hybrid video coding. However, unlike hybrid video codecs, existing deep video codecs cannot offer both flexible rates and regulable complexities within one single codec, which limits their applications. In this paper, we propose a Regulable Deep Video Codec (RDVC) to address the above issue. First, we propose an Adaptive Feature Compression (AFC) network that generates variable rates while ensuring Rate-Distortion (RD) performance. The network introduces a two-stage coarse-to-fine rate adjustment that can be controlled by a user-specified rate level. Second, we propose a Spatio-Temporal Feature Propagation (STFP) mechanism to provide high-quality reference information for AFC process. Third, we also utilize slimmable convolutional components in our framework to adjust decoding complexity constrained by user configuration. Experimental results demonstrate that RDVC can adjust the codec structure flexibly according to different user configurations while maintaining advanced performance. On average, it reduces the bit-per-pixel (bpp) by 9.35%/58.12% while maintaining the same PSNR/MS-SSIM as the reference software VTM-13.2. Sourcecode is available at: https://***/WXJ0001/RDVC. © 2025 IEEE.

关键词： Signal distortion

来源：评论

学校读者我要写书评

暂无评论

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal

arXiv

引用

arXiv 2022年

作者： Lin, Junhong Jiang, Nanfeng Zhang, Zhentao Chen, Weiling Zhao, Tiesong The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China The Peng Cheng Laboratory Shenzhen518055 China

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a LaplaceVQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-the-art snow removal quality with significantly reduced parameters and the lowest running time. Codes and models are available at https://***/StephenLinn/LMQFormer. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

arXiv

引用

arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Effective VVC Intra Prediction Based on Ensemble Learning

Effective VVC Intra Prediction Based on Ensemble Learning

引用

Picture Coding Symposium, PCS

作者： Hongji Zeng Yuhang Huang Tiesong Zhao Ludi Wu Weize Feng Guowei Cai Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China Peng Cheng Laboratory Shenzhen China Fujian AeroTiger UAV Co. Ltd Fujian China

ISBN: (纸本)9781665492584

This paper proposes a fast VVC coding unit partition algorithm based on ensemble convolutional neural network (CNN) by investigating and bagging spatial-temporal adjacent coding features. First, we propose an ensemble CNN framework to aggregate the reference features to predict the depths of uncoded CUs. The proposed model consists of three lightweight CNNs, which can compromise prediction accuracy with overhead. Then a majority voting mechanism is used to unify the predicted depth. By extracting the majority prediction of base learners, the outputs of three CNNs are integrated to obtain the final prediction. To avoid Rate Distortion (RD) loss caused by a small probability of prediction failure, we introduce the optimal depth strategy. During the encoding process, the optimal depth is used for the decision-making of coding unit partition, thus avoiding redundant rate distortion optimization process. Compared with the original encoder, the proposed algorithm saves 21.56% encoding time on average, with a BDBR loss of 0.39%. The performance is even superior in High-Definition (HD) and Ultra HD (UHD) sequences, up to 59.52%. This approach has a great efficiency of time reduction compared with state-of-the-arts with negligible RD performance loss.

关键词： Video coding Rate-distortion Predictive models Prediction algorithms Encoding Partitioning algorithms Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave Systems

arXiv

引用

arXiv 2023年

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

关键词： Millimeter waves

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：