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An Improved Compressive Sensing-Based Model for Efficient Analysis of Monostatic Electromagnetic Scattering Problems

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 388-391页

作者： Qi, Qi Fan, Yunuo Cao, Xinyuan Liu, Yi Kong, Meng Huang, Zhixiang Wu, Xianliang Hefei Normal University Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

To further accelerate the analysis of monostatic electromagnetic (EM) scattering problems of objects, an improved compressive sensing (CS)-based model is proposed. First, an orthogonal subspace spanned by the wide-angle induced currents is identified by means of the wide-angle incident sources. Then, the sparse transform of the induced currents is replaced by the subspace projection. In addition, an optimal number of measurements is determined, only slightly larger than the dimension of the subspace. Finally, the recovery algorithm is simplified to the least-squares method to solve the resulting overdetermined system. Compared with the traditional CS-based model, the proposed model significantly improves the efficiency of the recovery step and optimizes both the requirement for and the way of determining the number of measurements. © 2023 IEEE.

关键词： Least squares approximations

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Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 893-913页

作者： Lei Zhang Yutong Liu Haipeng Yang Fan Cheng Qi Liu Xingyi Zhang Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of EducationSchool of Computer Science and TechnologyAnhui UniversityHefeiChina School of Computer Science and Technology University of Science and Technology of ChinaHefeiChina

Influence maximization,whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network,has many applications such as goods advertising and rumour *** the existing influence maximization methods,the community‐based ones can achieve a good balance between effectiveness and ***,this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping *** fact,many nodes in social networks are overlapping ones,which play more important role in influence *** this end,an overlapping community‐based particle swarm opti-mization algorithm named OCPSO for influence maximization in social networks,which can make full use of overlapping nodes,non‐overlapping nodes,and their interactive information is ***,an overlapping community detection algorithm is used to obtain the information of overlapping community structures,based on which three novel evolutionary strategies,such as initialisation,mutation,and local search are designed in OCPSO for better finding influential *** results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts(***,CMA‐IM,CIM,CDH‐SHRINK,CNCG,and CFIN).

关键词： computational intelligence data mining social network

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Few-Shot Object Detection in SAR images via Gaussian Flow Class-Balanced Differential Aggregation 2

Few-Shot Object Detection in SAR images via Gaussian Flow Cl...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Zhao, Po Chen, Jie Zhang, Chaochen Ma, Ke Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Hefei230601 China

ISBN: (纸本)9798331515669

Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in SAR images to significant changes in orientation, make existing models sensitive to variations in novel class examples. This sensitivity leads to challenges in extracting class prototypes and accurately estimating class centers for novel classes. To solve the above issues, we propose a few-shot object detection algorithm for SAR images via Gaussian Flow Class-Balanced Differential Aggregation. Specifically, we first design a class-balanced difference feature aggregation module to solve the problems of difficult class distribution estimation and inaccurate class center estimation of novel classes in SAR images. Second, the contextual information perception module is designed to extract the most representative support features into fine-grained prototypes and aggregate them with the query features using the matching paradigm to solve the imbalance between foreground targets and background in SAR images. Finally, experimental results on MSAR-AIR dataset indicate that our model realizes the best detection properties on SAR images compared to existing models. © 2024 IEEE.

关键词： few-shot learning object detection synthetic aperture radar variational feature learning

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Mixture of Experts for Audio-Visual Learning 38

Mixture of Experts for Audio-Visual Learning

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Cheng, Ying Li, Yang He, Junjie Feng, Rui School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

With the rapid development of multimedia technology, audio-visual learning has emerged as a promising research topic within the field of multimodal analysis. In this paper, we explore parameter-efficient transfer learning for audio-visual learning and propose the Audio-Visual Mixture of Experts (AVMoE) to inject adapters into pre-trained models flexibly. Specifically, we introduce unimodal and cross-modal adapters as multiple experts to specialize in intra-modal and inter-modal information, respectively, and employ a lightweight router to dynamically allocate the weights of each expert according to the specific demands of each task. Extensive experiments demonstrate that our proposed approach AVMoE achieves superior performance across multiple audio-visual tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, visual-only experimental results also indicate that our approach can tackle challenging scenes where modality information is missing. The source code is available at https://***/yingchengy/AVMOE. © 2024 Neural information processing systems foundation. All rights reserved.

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A Fine Segmentation Method for the Outer Boundary of Tire Image Steel Belt Based on Adaptive Thresholding Under Local Histogram Statistical Features

A Fine Segmentation Method for the Outer Boundary of Tire Im...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Liu, Hua Li, Nan Hao, Jinyi Xia, Yingjie Li, Jinping School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-Year Jinan250022 China

ISBN: (纸本)9789819708543

The accuracy of defect detection in various locations is directly affected by the influence of precise segmentation of radial tire images. As the need for high-quality tires has grown, so has interest in the steel belt region. This region contains a diversity of textures and is impacted by shadows, and only a rough segmentation of this region has been achieved in the available literature, but this is interfered with by other areas, resulting in unsatisfactory defect detection in this region. As a result, we present a fine segmentation method for boundary mutation that takes advantage of the projection property of the local narrow window. First, the histogram adhesion change detection algorithm is designed;next, the narrow sliding window’s histogram peak width change algorithm is designed;finally, a wide sliding window is used for vertical projection, and the absolute value of the histogram change rate at the boundary is designed to maximize the algorithm. To calculate the outside boundary of the steel belt, the difference between the three sets of coordinates is kept within a specific error range. The detection was performed on 1,000 self-constructed data sets, with an accuracy rate of 94% and a detection speed of 0.1 s The experiments show that the algorithm can segment the steel belt’s outer boundary more finely and robustly, and it has a strong segmentation effect for images with a near texture angle and tire sidewall cord angle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textures

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A NEURAL MULTIGRID SOLVER FOR HELMHOLTZ EQUATIONS WITH HIGH WAVENUMBER AND HETEROGENEOUS MEDIA

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SIAM Journal on Scientific computing 2025年第3期47卷 C655-C679页

作者： Cui, Chen Jiang, Kai Shu, Shi Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

In this paper, we propose a deep learning-enhanced multigrid solver for high-frequency and heterogeneous Helmholtz equations. By applying spectral analysis, we categorize the iteration error into characteristic and noncharacteristic components. We eliminate the noncharacteristic components by a multigrid wave cycle, which employs carefully selected smoothers on each grid. We diminish the characteristic components by a learned phase function and the approximate solution of an advection-diffusion-reaction (ADR) equation, which is solved using another multigrid V-cycle on a coarser scale, referred to as the ADR cycle. The resulting solver, termed Wave-ADR-NS, enables the handling of error components with varying frequencies and overcomes constraints on the number of grid points per wavelength on coarse grids. Furthermore, we provide an efficient implementation using differentiable programming, making Wave-ADR-NS an end-to-end Helmholtz solver that incorporates parameters learned through semisupervised training. Wave-ADR-NS demonstrates robust generalization capabilities for both in-distribution and out-of-distribution velocity fields of varying difficulty. Comparative experiments with other multigrid methods validate its superior performance in solving heterogeneous 2D Helmholtz equations with wavenumbers exceeding 2000. © 2025 Society for Industrial and Applied Mathematics Publications. All rights reserved.

关键词： Helmholtz equation

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Video Rumor Classification Based on Multi-modal Theme and Keyframe Fusion 17th

Video Rumor Classification Based on Multi-modal Theme and ...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： You, Jinpeng Lin, Yanghao Lin, Dazhen Cao, Donglin Artificial Intelligence Department Xiamen University Xiamen361005 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China

ISBN: (纸本)9789819923557

In recent years, short video platforms have become the main source of online rumors. According to the statistics of Shanghai online rumor refutation platform in 2021, the number of short video rumors was about five times that of short video rumors in 2020, which makes it necessary to detect rumors of short videos. At present, short video rumor detection has the problem of multi-modal information fusion, the traditional multi-modal fusion uses deep learning to obtain the underlying features of multi-modality and then aggregate them into cross-modal features. However, there are distortions of theme and tampering with key-frame in rumor videos. Therefore, short video rumors need to learn features from the perspective of theme and key-frame. Aiming at the problem of multi-modal information fusion of short video rumors, this paper proposes a short video rumor detection model (TKCM) based on theme and key-frame. It uses aggregation network to obtain the theme feature of video, attention network to obtain the key-frame feature, and fuses multi-modality by modal adjustment mechanism for short video rumor detection. Experimental results show that the F1 score of the proposed method on the short video rumor dataset is improved by 2%–5% compared with some state-of-the-art video classification models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

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IEEE Transactions on Affective computing 2024年第2期16卷 624-638页

作者： Chen, Yin Li, Jia Shan, Shiguang Wang, Meng Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations, e.g., insufficient quantity and diversity of pose, occlusion and illumination, as well as the inherent ambiguity of facial expressions. In contrast, static facial expression recognition (SFER) currently shows much higher performance and can benefit from more abundant high-quality training data. Moreover, the appearance features and dynamic dependencies of DFER remain largely unexplored. Recognizing the potential in leveraging SFER knowledge for DFER, we introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features, thereby significantly improving DFER performance. Firstly, we build and train an image model for SFER, which incorporates a standard Vision Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling Adapters (TMAs) into the image model. MCPs enhance facial expression features with landmark-aware features inferred by an off-the-shelf facial landmark detector. And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters (less than +10%) to the original image model. Moreover, we present a novel Emotion-Anchors (i.e., reference samples for each emotion category) based Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion labels, further enhancing our S2D. Experiments conducted on popular SFER and DFER datasets show that we have achieved a new state of the art. © 2010-2012 IEEE.

关键词： Face recognition

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Leveraging Slot-Aware Enhancements for Improved Zero-Shot Dialogue State Tracking 6

Leveraging Slot-Aware Enhancements for Improved Zero-Shot Di...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Song, Tianhang Hu, Haolei Chen, Xinxin Zi, Kangli Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Task-oriented dialogue systems (TOD) aim to help users complete specific tasks through multiple rounds of dialogue, in which Dialogue State Tracking (DST) is a key component. The training of DST models typically necessitates the availability of a substantial corpus of dialogue data with turn-level annotations. However, the collection of such data is both costly and inefficient. Despite the impressive performance of systems based on advanced large language models (LLMs), their closed-source nature restricts the potential for local deployment. To address this challenge, this paper proposes a new zero-shot slot-aware enhanced model (SAE), which introduces a novel coded prompting framework to enhance the DST capability of small parameter models in new domains and task scenarios. In particular, we have devised a prompting framework that employs code to refine task instructions. Furthermore, we have proposed a slot-aware question generation mechanism that generates slot-aware questions based on contextual details in accordance with the dialogue context, thereby enhancing the model's perception and matching capabilities for slots. In order to verify the effectiveness of the model, we conducted experiments on the MultiWOZ dataset. The results show that the SAE model has achieved significant performance improvements in zero-shot DST tasks. © 2024 IEEE.

关键词： Zero-shot learning

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation 48

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Wenjing Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc Beijing100085 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods. © 2023 IEEE.

关键词： Convolutional neural network Deep refinement Multi-decision Ultrasound image segmentation

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