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

作者： Zihan Xu Zheng Wang Haoyu Wang Cheng Liu Yan Zhou Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Machine Learning Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

To adapt to the resource-limited environment, this study introduces the lightweight boundary-aware camouflaged object detection(COD) network LMABnet. We enhance the feature representation capability of the lightweight network through a multi-scale feature fusion architecture, while effectively avoids the model inflation and parameter redundancy of traditional COD methods. The LMABnet adopts the streamlined two-branch architecture that incorporates with the lightweight feature extraction module, which provides the multi-scale feature perception capability and reduces the loss of spatial information caused by rapid downsampling. In addition, based on the principle of "easy before difficult", the lightweight multi-scale feature fusion module discovers the key parts of the camouflaged object through the details. Then enhances the recognition of the object edges by using the edge-attention fusion module, so as to improve the accuracy performance of detection. Empirical evaluations conducted on the COD dataset demonstrate that the proposed methodology not only attains state-of-the-art performance but also accomplishes a significant reduction of 40% in parameter count relative to existing approaches.

关键词： Accuracy Image edge detection Scalability Redundancy Computer architecture Object detection Signal processing Feature extraction Real-time systems Speech processing

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Joint Edge and Regional Depth Enhancement Network for Camouflaged Object Detection

Joint Edge and Regional Depth Enhancement Network for Camouf...

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

作者： Miao Qi Zheng Wang Cheng Liu Yan Zhou Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Machine Learning Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Camouflaged object detection (COD) is a task of identifying and locating target objects that are camouflaged, masked, or confused. Research claims that depth cues can provide effective object location cues. However, depth images often contain noise interference, which may negatively affect object recognition. In addition, depth images also lack edge details, and most of previous works pay more attention to the integrity of the region, rather than the quality of the edge. To solve these two problems, we propose a joint edge and regional depth enhancement network (ERDENet) for Camouflaged Object Detection. The network first introduces the Locate and Generate Depth (LGD) module to locate the target region and generate its depth image. After that, the Feature Interactive Fusion (FIF) module is carried out, complementing the enhanced depth feature with the feature extracted from the original image, and then fuse the edge clues into it. Finally, we design a Muiti-modal Refinement Extraction (MRE) module to refine the feature to improve the detection performance. Extensive experiments show that our method has advantages and effectiveness.

关键词： Fuses Image edge detection Noise Object detection Interference Feature extraction Acoustics Object recognition Speech processing

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Multi-scale Re-weighted Attention Feature Fusion for Non-Intrusive Load Monitoring

Multi-scale Re-weighted Attention Feature Fusion for Non-Int...

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

作者： Lingxi Yang Meijun Sun Haowei Ran Yipu Liu Yan Zhou Zheng Wang College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Machine Learning Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Non-Intrusive Load Monitoring (NILM) addresses the challenge of disaggregating total energy consumption into individual appliance usage, which is essential for enhancing energy efficiency and managing smart grids. Existing methods often overlook the impact of window sizes on the separation of appliance-level power signals, leading to appliance power aliasing (APA), where a single total power curve corresponds to multiple operating conditions and appliance couplings. A larger window captures global features noise, but it also exacerbates APA, thereby enlarging the solution space. In contrast, a smaller window mitigates APA but it introduces noise and increases complexity. To address these limitations, we propose the Multi-Scale Re-weighted Attention Feature Fusion (MRAFF) model, which employs a multi-scale framework to manage temporal variations and incorporates a re-weighted attention module to enhance multi-scale interactions, thereby improving feature representation. Experimental results demonstrate that our MRAFF model significantly outperforms baseline models on the REDD and UK-DALE datasets, achieving improvements ranging from 2.27% to 10.5% in MAE and from 33.74% to 58.34% in SAE.

关键词： Couplings Load monitoring Energy consumption Noise Energy efficiency Distance measurement Smart grids Complexity theory Speech processing Load modeling

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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

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Optics Express 2025年第11期33卷 22208-22221页

作者： Chen-Rui Fan Bo Lu Yi-Xuan Yao Qing Ai Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing 100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou 450001 China lubo@*** School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing 100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing 100875 China wangchuan@***

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications.

关键词： Neural networks Nonlinear optical fibers Numerical simulation Optical parametric oscillators Optical systems Quantum noise

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Class Semantic Prompts Enhanced Prototypical Fusion Method for Few-shot Named Entity Recognition

Class Semantic Prompts Enhanced Prototypical Fusion Method f...

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

作者： Mei Yu Yuang Tao Mankun Zhao Tianyi Xu Zechen Meng Wenbin Zhang Jian Yu School of Future Technology Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China Information and Network Center Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Few-shot named entity recognition is to identify named entities in scenarios where labeled data is scarce. Existing prototype building methods ignore the use of class semantic and it is difficult to obtain accurate prototype representations only by relying on few support samples. In this paper, we propose a class semantic prompts enhanced prototypical fusion method (CSFP). Specifically, we design a class-semantic prototype that adapts to current task through prompts. In order to add intra-class similarity to the existing prototype and obtain a more accurate and stable prototype representation, we consider the samples distribution and fuse class-semantic prototype with existing prototype through a weighted strategy. Experimental results on two few-shot NER benchmarks show that our method outperforms previous SOTA methods. The analysis further verifies the effectiveness of our method.

关键词： Accuracy Fuses Semantics Buildings Prototypes Named entity recognition Signal processing Benchmark testing Acoustics Speech processing

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Multi-Granular Multimodal Clue Fusion for Meme Understanding

arXiv

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arXiv 2025年

作者： Zheng, Li Fei, Hao Dai, Ting Peng, Zuquan Li, Fei Ma, Huisheng 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 National University of Singapore Singapore Singapore Laboratory for Advanced Computing and Intelligence Engineering Wuxi China North China Institute of Computing Technology Beijing China

With the continuous emergence of various social media platforms frequently used in daily life, the multimodal meme understanding (MMU) task has been garnering increasing attention. MMU aims to explore and comprehend the meanings of memes from various perspectives by performing tasks such as metaphor recognition, sentiment analysis, intention detection, and offensiveness detection. Despite making progress, limitations persist due to the loss of fine-grained metaphorical visual clue and the neglect of multimodal text-image weak correlation. To overcome these limitations, we propose a multi-granular multimodal clue fusion model (MGMCF) to advance MMU. Firstly, we design an object-level semantic mining module to extract object-level image feature clues, achieving fine-grained feature clue extraction and enhancing the model’s ability to capture metaphorical details and semantics. Secondly, we propose a brand-new global-local cross-modal interaction model to address the weak correlation between text and images. This model facilitates effective interaction between global multimodal contextual clues and local unimodal feature clues, strengthening their representations through a bidirectional cross-modal attention mechanism. Finally, we devise a dual-semantic guided training strategy to enhance the model’s understanding and alignment of multimodal representations in the semantic space. Experiments conducted on the widely-used MET-MEME bilingual dataset demonstrate significant improvements over state-of-the-art baselines. Specifically, there is an 8.14% increase in precision for offensiveness detection task, and respective accuracy enhancements of 3.53%, 3.89%, and 3.52% for metaphor recognition, sentiment analysis, and intention detection tasks. These results, underpinned by in-depth analyses, underscore the effectiveness and potential of our approach for advancing MMU. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A Prompt Learning Framework with Large Language Model Augmentation for Few-shot Multi-label Intent Detection

A Prompt Learning Framework with Large Language Model Augmen...

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

作者： Ning Zhuang Xiao Wei Junlei Li Xiaobao Wang Chenyang Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Huiyan Technology (Tianjin) Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Intent detection (ID) is essential in spoken language understanding, especially in multi-label settings where intent labels are interdependent and diverse. Existing methods like SE-MLP and QA-FT struggle in few-shot settings, due to limited data availability and efficiency concerns. To address this, we introduce a Prompt Learning framework with large language Model Augmentation (PLMA) for few-shot multi-label ID. In this study, we make three contributions. First, PLMA integrates large language models (LLMs) with small language models (SLMs), using LLMs to enhance SLMs, with prompt learning as the core framework. Second, it leverages LLMs to improve the model’s understanding of both queries and labels by intent spans extraction and answer space expansion. Third, PLMA refines the QA-FT. A novel QA format template is designed to allow single-step training and inference for each input, improving efficiency. Experiments on NLU++ show that PLMA significantly outperforms other baselines in both in-domain and cross-domain settings, demonstrating its effectiveness in combining the strengths of both small and large models.

关键词： Training Adaptation models Accuracy Large language models Signal processing Natural language processing Acoustics Multilingual Speech processing

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Progressive Residual Extraction Based Pre-Training for Speech Representation Learning

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1825-1837页

作者： Tianrui Wang Jin Li Ziyang Ma Rui Cao Xie Chen Longbiao Wang Meng Ge Xiaobao Wang Yuguang Wang Jianwu Dang Nyima Tashi Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Huiyan Technology (Tianjin) Company Ltd. Tianjin China Saw Swee Hock School of Public Health National University of Singapore Singapore Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Information Science and Technology Tibet University Lhasa China State Key Laboratory of Tibetan Intelligence Lhasa China

Self-supervised learning (SSL) has garnered significant attention in speech processing, particularly excelling in linguistic tasks such as speech recognition. However, improving the performance of pre-trained models across various downstream tasks—each requiring distinct types of speech information—remains a significant challenge. To address this, we propose a progressive residual extraction based SSL method, named ProgRE. Specifically, we introduce two lightweight, specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to mitigate the incompatibility between the reinforced pitch variation and speaker information and the learning of content information, we employ residual extraction, leveraging the extracted representations as references or conditioning signals to guide the subsequent modules in more effectively learning content-related information under the supervision of HuBERT-based speech masking prediction. In this manner, we can incrementally extract pitch variation, speaker, and content representations from the input speech. Finally, these multiple representations, each capturing diverse speech information, are combined using different layer weights to produce task-specific representations for various downstream tasks. Experimental results demonstrate that our ProgRE achieves significant performance improvements across several tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, outperforming excellent SSL methods like wav2vec2.0, HuBERT, and WavLM.

关键词： Data mining Feature extraction Transformers Speech recognition Training Speech enhancement Acoustics Vector quantization Self-supervised learning Germanium

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WMRE: Enhancing Distant Supervised Relation Extraction with Word-level Multi-instance Learning and Multi-hierarchical Feature

WMRE: Enhancing Distant Supervised Relation Extraction with ...

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

作者： Xiang Ying Xiangchuan Xie Tianyi Xu Yue Zhao Zechen Meng Mankun Zhao College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of New Media and Communication Tianjin University Tianjin China Information and Network Center Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Distant supervised relation extraction (DSRE) obtains large amounts of data cost-effectively by aligning knowledge base with natural texts but also brings noisy data. Existing methods deal with noise through multi-instance learning (MIL) with attention. However, these approaches typically use attention at sentence-level and above, while ignoring word-level information. Intuitively, words in the same sentence are also of different importance. However, effective methods for distinguishing word-level importance differences are still lacking. Because multiplying with weighted attention leads word embeddings to be shifted in vector space. Therefore, we proposed WMRE. Specifically, WMRE concatenates the embeddings of multiple sentences. Then it uses filtering attention to remove low-importance words at different levels to extract multi-hierarchical features. In WMRE, words are the smallest units of mining information, and filtering attention distinguishes word-level importance differences while avoiding the embedding shifts. Extensive experiments on two widely used datasets show that WMRE fully utilizes word-level information and achieves state-of-the-art (SOTA) on both datasets.

关键词： Filtering Noise Knowledge based systems Feature extraction Vectors Acoustics Data mining Noise measurement Speech processing

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FGDC: A fine-grained divide-and-conquer approach for extending NCO to solve large-scale Traveling Salesman Problem

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Expert Systems with Applications 2025年 286卷

作者： Chen, Xinwei Li, Yurui Yang, Yifan Zhang, Li Li, Shijian Pan, Gang College of Computer Science and Technology Zhejiang University Hangzhou310027 China Advanced Technology Research Institute Zhejiang University Hangzhou310027 China State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou310027 China

Large-scale Traveling Salesman Problem (TSP) applications are common and important in practice. Unfortunately, the time usage of the state-of-the-art heuristic solver LKH increases quadratically with the scale of the problem instance. Neural Combinatorial Optimization (NCO) has emerged as an efficient alternative to LKH, but it struggles to handle large-scale instances using zero-shot generalization. Divide-and-conquer is a classical paradigm for handling large-scale problems. However, integrating NCO with the divide-and-conquer paradigm is challenging. Fine-grained division is necessary to maintain the solution quality of the NCO solver on sub-problems. The K-Means algorithm is widely used for division as it delivers good results, but its time complexity grows to O(N2) in fine-grained division. Besides, merging strategies that rely on pre-defined rules miss opportunities to improve solution quality. In this paper, we present FGDC, a fine-grained divide-and-conquer approach for extending NCO to solve large-scale TSP. In the dividing procedure, we propose LocKMeans algorithm with O(N) time complexity to construct small-scale sub-problems based on the density distribution of nodes. The solving procedure imposes minimal constraints on the NCO solver, which is used to solve these sub-problems with GPU parallel execution, allowing FGDC to serve as a plug-and-play tool for general NCO approaches. We propose an MST-based merging strategy which is enhanced from three different perspectives including merging combination, merging operator, and merging order. Experimental results demonstrate that FGDC outperforms existing methods in the fine-grained division scenario. Additionally, it is highly scalable over instances ranging from 1K to 1M nodes. When employing POMO (pre-trained on TSP-100) as the solver, FGDC surpasses the SOTA baseline H-TSP by a significant margin, yielding the best result for large-scale TSP within the NCO landscape. © 2025 Elsevier Ltd

关键词： Traveling salesman problem

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