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检索条件"任意字段=IEEE Data Compression Conference"

共 16713 条记录，以下是141-150 订阅

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Cooperative Multi-Modal Semantic Communication Scheme for Semantic Segmentation in Autonomous Driving Systems

Cooperative Multi-Modal Semantic Communication Scheme for Se...

引用

ieee conference on Wireless Communications and Networking

作者： Yunqi Feng Hesheng Shen Xiufang Shi Qianqian Yang College of Control Science and Engineering Zhejiang University Hangzhou China College of Information Engineering Zhejiang University of Technology Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In recent years, multi-modal semantic segmentation in autonomous driving has garnered significant attention due to its effectiveness under challenging lighting conditions. However, current segmentation approaches primarily focus on segmentation techniques without addressing the critical communication challenges inherent in internet of vehicles (IoV). Unlike traditional communications that transmit source data, semantic communications transmit only task-relevant semantic information, significantly reducing data traffic while ensuring the accuracy of task execution. This paper introduces a novel cooperative multi-modal semantic communication framework designed to enhance semantic segmentation in autonomous driving systems. By compressing redundant information and transmitting only essential semantic features, the proposed scheme enables continuous data transmission with drastically reduced data volume. Moreover, this scheme not only improves communication efficiency but also ensures reliable segmentation performance across diverse data modalities. Experimental results validate the effectiveness of proposed scheme, demonstrating its ability to achieve high compression ratios, robust segmentation performance, and re-silience to channel noise under varying lighting conditions.

关键词： Image coding Semantic segmentation Lighting Receivers Semantic communication Robustness data communication Autonomous vehicles Signal to noise ratio Internet of Vehicles

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K-HashFed: Communication Efficient Federated Learning through Gradient Clustering and Hashing

K-HashFed: Communication Efficient Federated Learning throug...

引用

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

作者： Ayshika Kapoor Dheeraj Kumar Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee Uttarakhand India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated learning (FL) is a decentralized framework for collaborative model training while preserving data privacy. However, high communication overhead between clients and central server hinders FL scalability. This paper introduces K-HashFed, a novel gradient compression approach that integrates K-means clustering and locality-sensitive hashing (LSH) to minimize communication costs while preserving model accuracy. The proposed method clusters gradient updates and uses LSH to further merge similar centroids, to significantly reduce data transmission. Experiments conducted on benchmark datasets such as MNIST, Fashion MNIST, CIFAR-10, and CIFAR-100, demonstrates a significant reduction in data transmission—by orders of magnitude—compared to state-of-the-art methods, with minimal accuracy loss. The results validate K-HashFed as a communication-efficient, scalable solution for real-world FL systems.

关键词： Training Accuracy Federated learning Scalability Signal processing algorithms Signal processing Propagation losses Servers data communication Speech processing

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A Quality-Aware Sampling Framework for Efficient 3D Point Cloud Transmission

A Quality-Aware Sampling Framework for Efficient 3D Point Cl...

引用

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

作者： Puyue Hou Qi Yang Yue Li Yujie Zhang Jianchao Yang Yiling Xu Tiejun Huang Shanghai Jiao Tong University University of Missouri-Kansas City Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The large volume of data from the point cloud brings significant demands on network bandwidth. However, the current transmission framework only considers using lossy compression to control the size of data, while ignoring visually redundant information due to the setting of rendering devices. Based on the fact that point overlapping might occur for the case that a dense point cloud is rendered on a relatively low resolution 2D monitor, we propose a novel quality-aware sampling framework for point cloud transmission. When a target visual quality is determined, an optimal sampling module is designed to remove overlapped points with the help of a simple but effective quality model. By taking into account the impact of multiple factors (i.e., sampling, lossy compression, and client rendering resolution), this quality model can predict the final perceptual quality in the client. Based on a newly constructed dataset which consists of 420 samples, experiment results show that the proposed transmission framework can significantly reduce bandwidth cost (e.g., 6.10% to 84.43%) and processing time (e.g., 8.99% to 92.53%) without introducing noticeable distortion under certain rendering conditions, thus achieving higher bandwidth utilization and better real-time performance.

关键词： Point cloud compression Visualization Solid modeling Costs Three-dimensional displays Bandwidth Rendering (computer graphics) Real-time systems Speech processing Signal resolution

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Pre-Training of Auto-Generated Synthetic 3D Point Cloud Segmentation for Outdoor Scenes

Pre-Training of Auto-Generated Synthetic 3D Point Cloud Segm...

引用

ieee Winter Applications and Computer Vision Workshops (WACVW)

作者： Takayuki Shinohara National Institute of Advanced Industrial Science and Technology Tsukuba Japan

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Acquiring and annotating large datasets for the segmentation of outdoor 3D point clouds observed by airborne light detection and ranging (LiDAR) is resource-intensive and fraught with privacy concerns, limiting the availability of labeled training data. Pre-trained models can help to some extent but their effectiveness hinges on large datasets, and self-supervised learning faces data scarcity challenges. We propose a formula-driven auto-generated terrain and shape point cloud dataset for 3D point cloud segmentation tasks. Our synthetic dataset was created from diverse 3D models with variations in polygon types and shape similarity, and provides a high-quality pre-training alternative to existing datasets. Experiments reveal that models pretrained on our synthetic data outperform those trained from scratch and rival existing self-supervised learning methods. Our synthetic data aims to supplement the 3D point clouds observed by airborne LiDAR segmentation models and tackle the challenge of limited data availability in this field.

关键词： Point cloud compression Solid modeling Three-dimensional displays Shape Atmospheric modeling Training data Self-supervised learning Fractals data models Synthetic data

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A Transmitter-Model Unaware Generative Image compression Framework for Semantic Communication

A Transmitter-Model Unaware Generative Image Compression Fra...

引用

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

作者： Rongcan Zheng Xiaodan Song Xuguang Zuo Minxi Yang Dahua Gao Xuemei Xie Guangzhou Institute of Technology Xidian University Guangzhou China NETINT Technologies Shanghai China School of Artificial Intelligence Xidian University Xi’an China Pazhou Lab Huangpu Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unlike traditional bit-level data transmission methods, semantic communication focuses on conveying the meaning behind the data. Though promising results have been achieved, existing end-to-end learning-based semantic communication frameworks often require a synchronization of deep models between the transmitter and the receiver. Such design leads to tens of thousands models to be stored at receiver since different manufactures may optimize their own models. To address this problem, we propose a novel model-unaware generative image compression framework for semantic communication. It features at employing human-understandable multi-modality representations as an intermediate layer to enhance information transmission efficiency and semantic consistency. Our framework introduces a mask-based rate-distortion optimization module, which effectively removes low-relevance information for image generation and reduces the bit rate while maintaining semantic consistency. Experimental results demonstrate that the framework can still reconstruct high-quality images at very low bit rates, showcasing its potential for applications in modern communication systems.

关键词： Image coding Transmitters Bit rate Rate-distortion Receivers Information processing Semantic communication Synchronization Optimization Image reconstruction

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RAW data: A Key Component for Effective Deepfake Detection

RAW Data: A Key Component for Effective Deepfake Detection

引用

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

作者： Sahar Husseinil Jean-Luc Dugelay Department of Digital Security EURECOM Biot France Docaposte Biometrics Lab Biot France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current deepfake detection methods are prone to overfitting to specific deepfake artifacts and often struggle with genuine images that have undergone compression and other image processing operations. These processes can obscure indicators of forgery, leading to inaccurate decisions. This paper aims to redefine the boundary between real and fake images by narrowing the definition of authentic samples to a stage closer to the radiance of the scene as captured by the sensor, prior to any transformations by an Image Signal Processor (ISP). Our proposed method bypasses ISP processing steps, such as denoising, white balance, and demosaicing, which are embedded in camera hardware. This unaltered preservation makes raw data an ideal starting point for deepfake detection. Given the scarcity of large-scale datasets designed for training on raw images, we propose a methodological approach to train our model on raw image data. Our method demonstrated state-of-the-art performance on the CDF dataset and showed competitive results across other RGB domain deepfake detection datasets. The model developed in this study is available at https://***/DeepFaux/Deepfake-Detection-with-RAW-data.

关键词： Training Deepfakes Image coding Noise reduction Signal processing Hardware Forgery data models Speech processing Overfitting

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Effective Pruning and compression of CNNs with Exponential Sparsity

Effective Pruning and Compression of CNNs with Exponential S...

引用

Power Electronics, Computer Applications (ICPECA), ieee International conference on

作者： Eunice Kabulo Mwelwa Jun Zhang College of Automation Central South University Changsha China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Deep learning models are remarkably accurate and preformat, although they are frequently computationally expensive. Large quantities of memory and high-performance GPUs or TPUs are among the computing resources needed for training and deploying large-scale neural networks. Model optimization becomes indispensable. The process of enhancing deep learning models' effectiveness and efficiency is known as “model optimization.” In this work, the proposed method for optimization is pruning. Pruning has proven to be effective in increasing inference efficiency, decreasing model size, and speeding up training without compromising accuracy. This paper introduces a pruning method leveraging exponential sparsity within each convolutional layer that varies from min_sparsity to max_sparsity and subsequent compression through sparse representations. Secondly, in this approach instead of the entire kernel sliding across the input data, it performs the one-by-one convolution, selecting slices with the important weights in each filter's kernel according to the sparsity target of that convolutional layer and setting all the other slices to zeros. The obtained results from the one-by-one convolution will be summed together to obtain the output map. Finally, the sparse model is converted into a TFLite model which is a converted format that can be used efficiently on edge devices using TensorFlow Lite. A framework that has been developed and evaluated with TensorFlow, leading to a smaller model size, and quicker inference times, and minimal accuracy loss compared to the original models. This study emphasizes the benefits of structured pruning and examines the results obtained with a model based on Vgg16.

关键词： Training Deep learning Accuracy Convolution Sparse approximation Computational modeling Power electronics Sparse matrices Kernel Optimization

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A Point Cloud Downsampling Method Balancing Global and Local Shapes for 3D Object Classification

A Point Cloud Downsampling Method Balancing Global and Local...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Ryota Sugimoto Kenji Kanai Shiori Maki Jiro Katto Dept. of Computer Science and Communications Engineering Waseda University Tokyo Japan Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Three-dimensional (3D) spatial recognition using point cloud data imposes substantial communication and computational burdens due to the high-density data. Downsampling is commonly employed to reduce data volume but often struggles to balance the preservation of the overall structure and the retention of detailed areas, leading to accuracy degradation in tasks like object classification. Some methods consider downstream processing but prioritize overall geometry, which may result in the loss of local details. We propose a balanced downsampling method that preserves the overall shape while increasing point density in critical regions to maintain finer details. Based on the deep learning framework SampleNet, our method integrates Average Voxel Grid Sampling (AVG) and a modified Farthest Point Sampling (FPS') thereby creating balanced training point clouds. This approach reduces processing time while improving classification accuracy. Specifically, our method achieves a 1.23% improvement in classification accuracy at larger downsampling sizes (250–1000 points) compared to SampleNet, while maintaining similar processing times. These results demonstrate the effectiveness of our method in addressing accuracy degradation in 3D point cloud classification tasks.

关键词： Point cloud compression Degradation Training Geometry Deep learning Electric potential Accuracy Three-dimensional displays Shape Extremities

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Preliminary Evaluation of Spatial Features for Real-Time Importance Estimation in Point Cloud

Preliminary Evaluation of Spatial Features for Real-Time Imp...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Junya Sato Yuki Umemoto Takumi Miyoshi Taku Yamazaki Ryoichi Shinkuma Graduate School of Engineering and Science Shibaura Institute of Technology Saitama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

City digital twins enable artificial intelligence (AI)-empowered services in smart cities by leveraging city-scale point cloud data. However, real-time collection and utilization of point cloud for AI tasks incur significant communication and computational costs. A key strategy is to estimate important regions within real-time point cloud frames that contributes to specific AI tasks. This study introduces a spatial feature extraction method for real-time importance estimation between point cloud frames. The proposed method can calculate importance values in parallel for each voxel based on the difference in a spatial feature between past and current frames. Furthermore, experiments conducted with an actual device demonstrate the effectiveness of the model, including an analysis of the estimation results and real-time performance when applying various spatial features.

关键词： Point cloud compression Performance evaluation Laser radar Smart cities Estimation Feature extraction Real-time systems Digital twins Artificial intelligence Consumer electronics

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Extending Gaussian Splatting to Audio: Optimizing Audio Points for Novel-View Acoustic Synthesis

Extending Gaussian Splatting to Audio: Optimizing Audio Poin...

引用

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

作者： Masaki Yoshida Ren Togo Takahiro Ogawa Miki Haseyama Graduate School of Information Science and Technology Hokkaido University Faculty of Information Science and Technology Hokkaido University

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper proposes a novel method to extend Gaussian Splatting (3DGS) to the audio domain, enabling novel-view acoustic synthesis solely using audio data. While recent advancements in 3DGS have significantly improved novel-view synthesis in the visual domain, its application to audio has been overlooked, despite the critical role of spatial audio for immersive AR/VR experiences. Our method addresses this gap by constructing an audio point cloud from audio at source viewpoints and rendering spatial audio at arbitrary viewpoints. Experimental results show that our method outperforms existing approaches relying on audio-visual information, demonstrating the feasibility of extending 3DGS to audio.

关键词： Point cloud compression Visualization Three-dimensional displays conferences Spatial audio Virtual reality User interfaces Rendering (computer graphics) Spectrogram

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