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共 29921 条记录，以下是41-50 订阅

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Adaptive Multi-Scale Local Correction for Semi-Supervised 3D Medical Image Segmentation

Adaptive Multi-Scale Local Correction for Semi-Supervised 3D...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Xinqiang Lu, Wenhuan Zheng, Ke Xu, Junhai College of Intelligence and Computing Tianjin University Tianjin300350 China School of Software and Communication Tianjin Sino-German University of Applied Sciences Tianjin300350 China

ISBN: (纸本)9798350368741

In recent years, semi-supervised 3D medical image segmentation has gained significant attention. However, current methods often struggle with multi-scale voxel differences and overlook the importance of loss weight balancing. To address these issues, we propose an adaptive multi-scale local correction method (AMLC). Our key contributions are: (1) a multi-scale local correction module to accurately capture differences between 3D voxel blocks at various scales;(2) an adaptive weighting adjustment module that dynamically balances losses, improving robustness and training. Experiments show that AMLC significantly outperforms existing methods on the public dataset, demonstrating its effectiveness. © 2025 ieee.

关键词： adaptive weight adjustment medical image segmentation Multi-scale local correction

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Certainty-guided Reasoning and Refinement Network for Camouflaged Object Detection

Certainty-guided Reasoning and Refinement Network for Camouf...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Lai, Bifan Sun, Meijun Zhao, Junkun Zhou, Yan College of Intelligence and Computing Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (纸本)9798350368741

Camouflaged object detection (COD), which aims to segment objects that are highly similar to their background, is a valuable yet challenging task. Due to the interference of clutter and noise in the background, existing methods often struggle to avoid misleading and accurately segment the camouflaged object. In this paper, we propose a novel Certainty-guided Reasoning and Refinement Network (CRRNet) for COD. The core idea is to first explicitly reason under accurate knowledge of camouflaged objects, and then refine the uncertain regions by a certainty propagation strategy. To achieve this, we innovatively propose two modules: the Certainty-guided Reasoning Module and Uncertain Region Refinement Module. Experimental results prove that the proposed CRRNet outperforms 12 state-of-the-art methods on three COD benchmark datasets. © 2025 ieee.

关键词： camouflaged object detection certainty-guided reasoning image segmentation

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Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes

Kernel-Based Anomaly Detection Using Generalized Hyperbolic ...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Bourigault, Pauline Mandic, Danilo P. Department of Computing Imperial College London United Kingdom Department of Electronic and Electrical Engineering Imperial College London United Kingdom

ISBN: (纸本)9798350368741

We present a novel approach to anomaly detection by integrating Generalized Hyperbolic (GH) processes into kernel-based methods. The GH distribution, known for its flexibility in modeling skewness, heavy tails, and kurtosis, helps to capture complex patterns in data that deviate from Gaussian assumptions. We propose a GH-based kernel function and utilize it within Kernel Density Estimation (KDE) and One-Class Support Vector Machines (OCSVM) to develop anomaly detection frameworks. Theoretical results confirmed the positive semi-definiteness and consistency of the GH-based kernel, ensuring its suitability for machine learning applications. Empirical evaluation on synthetic and real-world datasets showed that our method improves detection performance in scenarios involving heavy-tailed and asymmetric or imbalanced distributions. https://***/paulinebourigault/GHKernelAnomalyDetect. © 2025 ieee.

关键词： anomaly detection generalized hyperbolic processes kernel

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End-to-end acoustic-articulatory dysarthric speech recognition leveraging large-scale pretrained acoustic features

End-to-end acoustic-articulatory dysarthric speech recogniti...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yue, Zhengjun Zhang, Yuanyuan Multimedia Computing Group Delft University of Technology Netherlands

ISBN: (纸本)9798350368741

Automatic dysarthric speech recognition (ADSR) remains challenging due to the irregularities in speech caused by motor control impairments and the limited availability of dysarthric speech data. This paper explores the integration of articulatory features, captured using Electromagnetic Articulography (EMA), with both conventional acoustic features and those extracted from large-scale pretrained models including Whisper and XLSR-53 as well as the fine-tuned Whisper model. We propose end-to-end (E2E) Conformer-based acoustic-articulatory models for ADSR and compare their performance against the corresponding hybrid TDNNF models. The experimental results show that using the fine-tuned Whisper features (Whisper-FT) fused with articulatory features achieves the lowest (10.5%) word error rate (WER) on dysarthric speech, with particularly significant improvements for severely dysarthric speech, reaching a WER of 20.8%. © 2025 ieee.

关键词： articulatory-acoustic multi-modal dysarthric speech recognition large-scale pretrained acoustic features

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The international Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 ieee.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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Generalized Graph signal Reconstruction via the Uncertainty Principle

Generalized Graph Signal Reconstruction via the Uncertainty ...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhao, Yanan Jian, Xingchao Ji, Feng Tay, Wee Peng Ortega, Antonio School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9798350368741

We introduce a novel uncertainty principle for generalized graph signals that extends classical time-frequency and graph uncertainty principles into a unified framework. By defining joint vertex-time and spectral-frequency spreads, we quantify signal localization across these domains, revealing a trade-off between them. This framework allows us to identify a class of signals with maximal energy concentration in both domains, forming the fundamental atoms for a new joint vertex-time dictionary. This dictionary enhances signal reconstruction under practical constraints, such as intermittent data, commonly encountered in sensor and social networks. Numerical experiments on real-world datasets demonstrate the effectiveness of the proposed approach, showing improved reconstruction accuracy and noise robustness compared to existing methods. © 2025 ieee.

关键词： generalized graph signal reconstruction joint vertex-time dictionary localized representation Uncertainty principle

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Harnessing Dimensional Contrast and Information Compensation for Sentence Embedding Enhancement

Harnessing Dimensional Contrast and Information Compensation...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： He, Kang Ding, Yuzhe Li, Bobo Wang, Haining Li, Fei Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798350368741

Unsupervised sentence embedding learning excels through positive sample construction and instance-level contrastive learning (ICL). However, this approach can lead to over-compression and dimensional contamination from noisy data augmentation and unconstrained ICL processes. To mitigate these issues, we propose a novel enhancement method, MSSE, which incorporates an Information Compensation Mechanism (ICM) and a Dimensional-Level Contrastive Learning Mechanism (DCM). ICM, inspired by the information bottleneck principle, prevents excessive compression in representation learning. DCM constrains ICL and minimizes information leakage across dimensions. Experimental results show that MSSE surpasses existing competitive baselines across seven STS tasks, including unsupervised and few-shot scenarios. The source code is available at https://***/Hekang001/MSSE. © 2025 ieee.

关键词： contrastive learning natural language processing Sentence embedding unsupervised learning

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 ieee.

关键词： Feature Fusion Infrared Small Target Segmentation Semantic Injection Wavelet Transformation

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A Distributed Task Allocation Methodology for Edge computing in a LEO Satellite IoT Context 12

A Distributed Task Allocation Methodology for Edge Computing...

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12th Advanced Satellite Multimedia Systems conference and the 18th signal processing for Space communications Workshop, ASMS/SPSC 2025

作者： Shinde, Swapnil Sadashiv Naseh, David Decola, Tomaso Tarchi, Daniele CNIT - University of Florence Research Unit Firenze50139 Italy University of Bologna Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" Bologna40136 Italy Institute of Communications and Navigation Weßling Germany University of Florence Department of Information Engineering Firenze50139 Italy

ISBN: (纸本)9798331522353

The Internet of Things (IoT) is one of the most promising applications in the field of computer networking. Edge computing is a computationally efficient method for processing user data in a terrestrial-satellite hybrid environment, where each device is connected exclusively through a low-elevation (LEO) satellite. This paper focuses on an IoT context, introducing methodologies to effectively manage the computation-communication trade-off by strategically distributing processing tasks across various satellites. In particular, an adaptive load balancing approach is considered for efficient utilization of satellite resources. The proposed method can be implemented in a distributed manner, enabling each satellite to evaluate its task handling capacity and forward tasks if it is beyond its capability. The numerical results demonstrate the effectiveness of the proposed method compared to conventional fixed allocation and cloud processing methodologies. © 2025 ieee.

关键词： Communication satellites

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Dynamically Optimize MTD Strategy in Satellite computing Systems Using A2C Reinforcement Learning

Dynamically Optimize MTD Strategy in Satellite Computing Sys...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Lin Guo, Yunchuan Guo, Shoukun Li, Fenghua Jiang, Faqun Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China Beijing Venustech Cybervision Co. Ltd. Beijing China

ISBN: (纸本)9798350368741

The Satellite computing System (SCS) faces an increasing number of attacks. Although Moving Target Defense (MTD) can effectively mitigate attacks in ground networks, it is not well-suited for SCS due to the highly dynamic nature of both SCS traffic and attackers' scanning behaviors. In this paper, we propose a dynamic MTD strategy optimization scheme using Advantage Actor-Critic (A2C) reinforcement learning. Specifically, we formulate the MTD strategy optimization for SCS as a Markov Decision Process (MDP). Furthermore, by accounting for the uncertainty in attack behavior changes, we apply A2C reinforcement learning to optimize the MTD strategy within the MDP framework. Experimental results demonstrate that our scheme effectively reduces the frequency of scanning hits, shortens the duration attackers can hold addresses, and minimizes the impact of MTD on quality of service. © 2025 ieee.

关键词： moving target defense reinforcement learning satellite computing system strategy optimization

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