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Memory-Free and parallel Computation for Quantized Spiking Neural Networks

Memory-Free and Parallel Computation for Quantized Spiking N...

引用

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

作者： Dehao Zhang Shuai Wang Yichen Xiao Wenjie Wei Yimeng Shan Malu Zhang Yang Yang School of Computer Science and Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight and membrane potential result in a notable performance decline. In this study, we first identify a new underlying cause for this decline: the loss of historical information due to the quantized membrane potential. To tackle this issue, we introduce a memory-free quantization method that captures all historical information without directly storing membrane potentials, resulting in better performance with less memory requirements. To further improve the computational efficiency, we propose a parallel training and asynchronous inference framework that greatly increases training speed and energy efficiency. We combine the proposed memory-free quantization and parallel computation methods to develop a high-performance and efficient QSNN, named MFP-QSNN. Extensive experiments show that our MFP-QSNN achieves state-of-the-art performance on various static and neuromorphic image datasets, requiring less memory and faster training speeds. The efficiency and efficacy of the MFP-QSNN highlight its potential for energy-efficient neuromorphic computing.

关键词： Training Performance evaluation Quantization (signal) Neuromorphic engineering Memory management Membrane potentials Spiking neural networks Energy efficiency Computational efficiency Speech processing

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Video-Poetry Retrieval with Multimodal Knowledge Graph Guided Unsupervised Pre-training

Video-Poetry Retrieval with Multimodal Knowledge Graph Guide...

引用

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

作者： Xinru Wei Yuqing Li Bin Wu School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Classical Chinese poetry, with its rich cultural heritage, holds immense artistic value. Recently, research on multi-modal approaches to classical poetry has garnered attention. Current research mainly focuses on poetry and images, but images cannot fully capture dynamic scenes like sunrises. However, the inherent temporal nature of video can vividly present these scene transitions. In this paper, we introduce a novel task: video-poetry retrieval. Due to the disparities between classical Chinese poetry and modern Chinese, and the time-consuming nature of traditional supervised methods for collecting parallel data. We propose VPR-MLCL, a video-poetry pre-training model based on unsupervised pre-training methods from vision-language field to address these issues. VPR-MLCL uses a multi-modal knowledge graph of classical Chinese poetry to bridge the gap, learning cross-modal alignment from video-caption pairs and poetry-image entities rather than direct video-poetry pairs. Additionally, we have constructed a video-poetry dataset for this task, and experiments demonstrate the effectiveness of VPR-MLCL.

关键词： Knowledge graphs Signal processing Benchmark testing Acoustics Cultural differences Speech processing

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

引用

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

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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Subsampling Decomposition based k-Space Refinement for Accelerated MRI Reconstruction

Subsampling Decomposition based k-Space Refinement for Accel...

引用

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

作者： Xiaoyu Qiao Weisheng Li Bin Xiao Yuping Huang Lijian Yang Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we propose a novel deep unfolding network (DUN) with subsampling decomposition (SD) based k-space refinement to mitigate the ill-posedness. Our method employs a parallel network architecture with a primary branch unfolded by gradient descent-inspired optimization process (GD-PB) for reconstruction. Additionally, we introduce an SD-based auxiliary branch (SD-AB) that decompose the inverse problem into moderately corrupted subproblems. We design a novel subsampling mask predictor that captures both global and local spatial correlations in k-space, ensuring the SD-AB effectively preserves the most well-reconstructed subsets as reliable region (RR). The RR in SD-AB is used to periodically refine the intermediate outputs of the GD-PB, achieving improved accuracy. Experimental results reveal that our method significantly outperforms conventional and SD-based DUN techniques, achieving superior PSNR and SSIM results compared with cutting-edge methods.

关键词： Correlation Inverse problems Magnetic resonance imaging Signal processing Network architecture Reliability engineering Sensors Speech processing image reconstruction Optimization

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Non-Autoregressive Multimodal Machine Translation

Non-Autoregressive Multimodal Machine Translation

引用

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

作者： Guojing Liu Xiangqian Ding Huili Gong Xiangyu Qu Zhenyu Yang Kai Yan Faculty of Information Science and Engineering Ocean University of China Qingdao China School of Cyber Science and Technology Shandong University Qingdao China Shandong Academy of Sciences Qilu University of Technology Jinan China Laiwu Vocational and Technical College Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Performing better text translation by integrating auxiliary inputs from visual information has gained widespread attention in recent years. While existing methods outperform the text-only translation models, the step-by-step generative style reduces the inference speed, which limits their applicability in real-world scenarios. In this paper, we propose the non-autoregressive language model (NA-LM) for multimodal machine translation. With NA-LM, we develop a Non-Autoregressive Multimodal Transformer (NA-MMT), which accelerates the generative translation via a parallel multimodal decoder. To retain the translation performance, we improve the NA-MMT in twofold: 1) We preprocess the image into a refined sequence of visual entities with length encoding to reduce irrelevant information; 2) We design cross fertility and cross-modal gate attention for multimodal decoder to enhance the generative quality. Experiments on Multi30k datasets show the NA-MMT can generate high-quality translation with over 11× speedup than the baselines, which is strongly competitive.

关键词： Visualization Translation image coding Logic gates Signal processing Transformers Encoding Decoding Machine translation Speech processing

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

引用

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

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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Some Intriguing Observations on the Learnt Matrices in Deep Unfolded Networks

Some Intriguing Observations on the Learnt Matrices in Deep ...

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

作者： Kartheek Kumar Reddy Nareddy Inbasekaran Perumal Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bengaluru Institute of Data Science German Aerospace Center Jena Germany Department of Electronics and Communication Engineering National Institute of Technology Mangaluru Karnataka

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep-unfolded networks (DUNs) have set new performance benchmarks in fields such as compressed sensing, image restoration, and wireless communications. DUNs are built from conventional iterative algorithms, where an iteration is transformed into a layer/block of a network with learnable parameters. Despite their huge success, the reasons behind their superior performance over their iterative counterparts are not fully understood. This paper focuses on enhancing the explainability of DUNs by investigating potential reasons behind their superior performance over traditional iterative methods. We concentrate on the Learnt Iterative Shrinkage-Thresholding Algorithm (LISTA), a foundational contribution that achieves sparse recovery with significantly fewer layers than its iterative counterpart, ISTA. Our findings reveal that the learnt matrices in LISTA always have Gaussian distributed entries regardless of whether the sensing matrix is random Gaussian, Bernoulli, exponential, or uniform. The findings also show that the singular values of the learnt matrices exceed unity, despite which, the reconstruction scheme is stable. We conjecture that the activation function may have a role to play in ensuring stability. We also present an unbiasing technique that substantially improves the sparse recovery performance by reestimating the amplitudes based on the converged support.

关键词： Training Wireless communication Accuracy Superresolution Signal processing algorithms Gaussian distribution Iterative algorithms Sparse matrices image reconstruction Convergence

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JANE: Joint Angle Networks Assisting 3D Human Pose Estimation

JANE: Joint Angle Networks Assisting 3D Human Pose Estimatio...

引用

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

作者： Jinhuan Wang Yuzhen Zhao Xujie Song Wenzhou Chen Qi Xuan Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology HangZhou China Department of Computer Science and Technology Hangzhou Dianzi University HangZhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D human pose estimation (HPE) is crucial due to its extensive applications. While current 3D HPE methods focus on human skeleton topology for accuracy, they often overlook joint angle information, which is vital in 2D-to-3D pose lifting. This paper introduces the Joint Angle Network (JANE) model to leverage joint angle features from 2D poses. We propose an angle graph convolution module to extract joint angle features and a multi-stage parallel fusion module to integrate these features with input image data. Experiments on Human3.6M show that our method improves accuracy by 8.7% and 2.6% over non-spatiotemporal baselines using 2D ground truth and 2D pose detectors, respectively.

关键词： Visualization Solid modeling Three-dimensional displays Accuracy Convolution Pose estimation Feature extraction Transformers Topology Speech processing

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A Dual-Stream Network with Non-Stationary Characteristics-Enhanced for SST image Prediction

A Dual-Stream Network with Non-Stationary Characteristics-En...

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

作者： Min Ye Jiahao Du Ning Song Xinyue Liang Bohan Li Jie Nie Fac. of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sea surface temperature (SST) prediction is crucial for understanding global climate and marine ecosystems, and its anomalies can lead to extreme weather events. SST exhibits complex non-stationary over natural spatio-temporal processes. However, most of the existing deep learning methods for SST prediction only extract non-stationary features through the simple state transitions of classic CNNs or RNNs, which are too simplistic to capture higher-order non-stationary trends in complex SST sequences. Therefore, we propose a DSNet_SST network, aiming to enhance the extraction of non-stationary information from the spatio-temporal SST evolution. It incorporates two parallel modules: one for capturing high-order temporal non-stationarity based on stacked memory in memory (MIM) blocks and the other one for extracting spatial correlation non-stationarity by multiscale difference operation. A third module adaptively integrates these features to improve the accuracy and stability of SST prediction. The experimental results, relying on the OISST data obtained from both remote sensing satellites and in situ platforms, demonstrate the advantages of the proposed DSNet_SST over baseline methods in terms of SST prediction accuracy.

关键词： Temperature sensors Accuracy Signal processing Feature extraction Spatiotemporal phenomena Data mining Speech processing Thermal stability Surface treatment Meteorology

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parallel and distributed methods for image processing IV

Parallel and distributed methods for image processing IV

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2000年

作者： Hongchi Shi Patrick C. Coffield Divyendu Sinha

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