Neurocomputing最新文献_第5页

Boosting representation diversity in video transformers via segmented contrastive masked autoencoders 通过分段对比掩码自编码器提高视频转换器的表示多样性

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-13 DOI: 10.1016/j.neucom.2026.133058

Yawei Feng , Lijun Guo , Guitao Yu , Rong Zhang , Jiangbo Qian , Chong Wang , Shangce Gao

{"title":"Boosting representation diversity in video transformers via segmented contrastive masked autoencoders","authors":"Yawei Feng , Lijun Guo , Guitao Yu , Rong Zhang , Jiangbo Qian , Chong Wang , Shangce Gao","doi":"10.1016/j.neucom.2026.133058","DOIUrl":"10.1016/j.neucom.2026.133058","url":null,"abstract":"<div><div>In self-supervised representation learning, contrastive learning leverages data invariance to identify global patterns; however, its lack of local constraints often results in video representations that are overly focused on global content, thereby limiting feature diversity. To address this, we propose Segmented Contrastive Masked Autoencoders (SCMA). By extending the masked distillation paradigm to the spatiotemporal domain, our framework integrates contrastive learning with masked autoencoders via teacher-student self-distillation. SCMA enhances the capability of contrastive learning to capture local details in videos, effectively improving the diversity of video representations learned by ViT models. Furthermore, we introduce a Segmented Mask Sampling strategy to partition videos uniformly and apply adaptive masking, enhancing both training efficiency and robustness to input variations. Experimental results demonstrate that SCMA achieves superior top-1 accuracy on a range of video action recognition benchmarks under both fine-tuning and linear probing settings, while also demonstrating strong performance in video object segmentation.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133058"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic denoising track: Towards end-to-end multiple object tracking against attention trivialization 动态去噪跟踪：针对注意力琐碎化的端到端多目标跟踪

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-11 DOI: 10.1016/j.neucom.2026.132994

Ruonan Wei, Yuehuan Wang

{"title":"Dynamic denoising track: Towards end-to-end multiple object tracking against attention trivialization","authors":"Ruonan Wei, Yuehuan Wang","doi":"10.1016/j.neucom.2026.132994","DOIUrl":"10.1016/j.neucom.2026.132994","url":null,"abstract":"<div><div>Transformer-based architectures have emerged as a powerful paradigm for end-to-end multi-object tracking (MOT). However, existing frameworks typically employ static architectures with a fixed number of decoding layers, failing to adapt feature interaction levels to the varying complexities of tracking scenarios. This rigidity introduces a critical issue, which we define as attention trivialization, where excessive global interaction in deeper Transformer layers accumulates redundant noise and diminishes the discriminative power of attention. To mitigate this, we propose the Dynamic Denoising Tracking framework (DDTrack), which adaptively regulates interaction depth based on target-specific requirements. DDTrack incorporates two core components: Spatial-Temporal Early-Exit (<span><math><msup><mtext>STE</mtext><mn>2</mn></msup></math></span>) module and Self-Guided Denoising (SGD) module. The <span><math><msup><mtext>STE</mtext><mn>2</mn></msup></math></span> module mitigates noise propagation by terminating feature interactions early in the decoding process. It leverages spatio-temporal clues to perceive the complexity of foreground-background queries, enabling low-complexity samples to exit early and reduce the propagation of redundant information, thereby mitigating the attention trivialization of simple samples. The SGD module is designed for high-complexity targets. It utilizes cross-layer guidance to rectify attention trivialization. Specifically, shallow-layer queries serve as structural anchors to help deeper queries filter irrelevant noise and preserve target-specific information, thereby obtaining refined embeddings and highly discriminative attention. Finally, we jointly optimize inference depth and tracking performance through an end-to-end learning framework. Extensive experiments are conducted on DanceTrack, MOT17, and MOT20 benchmarks, achieving state-of-the-art results on several metrics and verifying the effectiveness of our proposed method.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 132994"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage line graph reasoning framework for multi-intent spoken language understanding 面向多意图口语理解的两阶段线形图推理框架

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-13 DOI: 10.1016/j.neucom.2026.133053

Shijie Duan , Long Yu , Shengwei Tian

{"title":"A two-stage line graph reasoning framework for multi-intent spoken language understanding","authors":"Shijie Duan , Long Yu , Shengwei Tian","doi":"10.1016/j.neucom.2026.133053","DOIUrl":"10.1016/j.neucom.2026.133053","url":null,"abstract":"<div><div>Spoken language understanding (SLU) is a core component of dialogue systems, responsible for intent detection and slot filling to interpret user utterances. We propose a two-stage line-graph-based framework for joint <em>multi-intent</em> SLU. In the first stage, our model uses cross-attention to encode token-level and intent-level features and leverages domain information to perform preliminary slot tagging and intent prediction. In the second stage, we construct an intent-slot graph from the first-stage outputs and then apply a line graph neural network to explicitly model interactions between each intent and its associated slots. This line graph reasoning mechanism reduces interference between multiple intents and enables effective bidirectional information flow, thereby enhancing semantic fusion of intent and slot cues. Experiments on the MixATIS and MixSNIPS benchmarks, as well as the challenging BlendATIS and BlendSNIPS datasets of blended utterances, demonstrate that the proposed approach outperforms state-of-the-art models, achieving higher slot-filling F<sub>1</sub>, intent classification accuracy, and overall sentence-level understanding accuracy.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133053"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synchronization of fractional-order delayed fuzzy memristive neural networks with unknown parameters and reaction-diffusion terms 具有未知参数和反应扩散项的分数阶延迟模糊记忆神经网络的同步

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.neucom.2026.133037

Haining Li , Hong-Li Li , Long Zhang , Hai Zhang , Yonggui Kao

引用次数: 0

GeomFlow: Geometry-aware adaptive diffusion model via Hessian information GeomFlow：基于Hessian信息的几何感知自适应扩散模型

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-06 DOI: 10.1016/j.neucom.2026.132934

Haozhuo Cao , Xiaorui Wang , Liyang Yu , Wangcai Ding

{"title":"GeomFlow: Geometry-aware adaptive diffusion model via Hessian information","authors":"Haozhuo Cao , Xiaorui Wang , Liyang Yu , Wangcai Ding","doi":"10.1016/j.neucom.2026.132934","DOIUrl":"10.1016/j.neucom.2026.132934","url":null,"abstract":"<div><div>Score-based generative models have achieved remarkable fidelity by employing stochastic differential equations (SDEs) to progressively transform data into noise. However, standard approaches suffer from a fundamental geometric mismatch: they apply spatially uniform diffusion dynamics across data manifolds that exhibit highly heterogeneous curvature. This “one-size-fits-all” strategy leads to inefficient sampling—over-computing in flat regions while under-resolving complex high-frequency details. To address this, we propose GeomFlow, a novel Geometry-Aware Adaptive Diffusion Model. GeomFlow synergizes two key mechanisms: a global learnable noise schedule that optimizes the macroscopic noise progression, and a geometric complexity estimator that utilizes a robust stochastic approximation of the Hessian trace to actively modulate local diffusion strength. Theoretically, we prove that our geometry-aware reverse process is equivalent to Riemannian preconditioned Langevin dynamics, enabling accelerated convergence and better escape from saddle points. Extensive experiments demonstrate that it achieves highly competitive performance on CIFAR-10 (FID 2.14) and CelebA-HQ, demonstrating superior structural understanding in conditional generation and image inpainting tasks with significant improvements in preserving semantic consistency and recovering missing texture details.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 132934"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CDNE: Community deception from node and edge perspectives 从节点和边缘的角度看社区欺骗

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-11 DOI: 10.1016/j.neucom.2026.133039

Yan Kang , Jiajun Tang , Baochen Fan , Hu Yuan

{"title":"CDNE: Community deception from node and edge perspectives","authors":"Yan Kang , Jiajun Tang , Baochen Fan , Hu Yuan","doi":"10.1016/j.neucom.2026.133039","DOIUrl":"10.1016/j.neucom.2026.133039","url":null,"abstract":"<div><div>As complex networks grow, community detection aids social network clustering but also risks exposing sensitive ties. Community deception alters network structures to hide target communities and protect privacy. Existing deception approaches primarily rely on either node-level or edge-level interventions, yet they often neglect the heterogeneous influence of individual nodes and edges, resulting in suboptimal concealment performance. To address these limitations, we propose CDNE, a novel community deception model from both node and edge perspectives. The model integrates node-based community deceptions into edge-based community deceptions for the first time, thus expanding the feasible manipulation space and enabling more flexible and effective deceptions. Moreover, we theoretically study the effects of inter-community and intra-community edge adding and deleting operations as a deception optimization function. Experiments on twenty-five community structure partitions generated by five real-world network datasets and five community detection algorithms show that CDNE consistently outperforms existing state-of-the-art deception methods.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133039"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-step transfer learning approach for railway point machine fault diagnosis under small sample conditions 小样本条件下铁路点机故障诊断的两步迁移学习方法

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.neucom.2026.133043

Tao Wen , Yixue Shen , Xia Fang , Zhongbei Tian , Clive Roberts

{"title":"A two-step transfer learning approach for railway point machine fault diagnosis under small sample conditions","authors":"Tao Wen , Yixue Shen , Xia Fang , Zhongbei Tian , Clive Roberts","doi":"10.1016/j.neucom.2026.133043","DOIUrl":"10.1016/j.neucom.2026.133043","url":null,"abstract":"<div><div>Fault diagnosis technology based on Transfer Learning (TL) has been widely investigated, as it enables knowledge transfer from related domains to the target domain, thereby reducing the need for large amounts of labeled data in the target domain. In the railway industry, Railway Point Machines (RPMs) are essential track-connection devices. However, due to the scarcity of fault data for RPMs, the accuracy of RPM fault diagnosis remains low. To address this challenge, this paper proposes a Two-step Transfer Learning (TSTL) method for RPM fault diagnosis. First, a small set of target-domain samples transformed by continuous wavelet transform (CWT) is used to construct a transition dataset, which is fed into a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to generate synthetic samples. Then, a Convolutional Neural Network (CNN) with a top-down attention mechanism is employed to perform the first phase of TL from the source domain to the generated domain. Next, the parameters of selected frozen layers in the transferred CNN are retained to conduct the second phase of learning from the generated domain to the target domain. Experimental results demonstrate that the proposed method achieves 98.7% accuracy for fault diagnosis across different positions on the same RPM, and 95.6% accuracy across different RPMs at the same position.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133043"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEMD-Net: A transformer based approach for brain tumor segmentation and classification SEMD-Net：基于变压器的脑肿瘤分割与分类方法

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-07 DOI: 10.1016/j.neucom.2026.132831

S. Vidhya , P.M. Siva Raja , R.P. Sumithra , Moses Garuba , Xiao-Zhi Gao

{"title":"SEMD-Net: A transformer based approach for brain tumor segmentation and classification","authors":"S. Vidhya , P.M. Siva Raja , R.P. Sumithra , Moses Garuba , Xiao-Zhi Gao","doi":"10.1016/j.neucom.2026.132831","DOIUrl":"10.1016/j.neucom.2026.132831","url":null,"abstract":"<div><div>Brain tumor segmentation and classification from MRI images are critical tasks in neuro-oncology, but they remain challenging due to tumor heterogeneity, ambiguous boundaries, and variability across multi-modal imaging sequences. Existing deep learning methods often struggle with subregion delineation and generalization, leading to incomplete or inaccurate results. In this paper, we propose Squeeze-and-Excitation Mamba with DeiSwin+ + (SEMD-Net), a unified deep learning framework designed to improve both tumor segmentation and imaging-based glioma grade classification (HGG vs. LGG). The model integrates a multi-branch learning architecture capable of capturing both global and localized tumor features. It combines spatial-channel attention mechanisms and transformer-based representation learning to address the challenges of boundary precision, tissue variability, and intra-class heterogeneity. We evaluated our method on the BraTS 2020 and 2021 datasets using five-fold cross-validation. SEMD-Net achieved strong performance on key segmentation metrics, including a mean Dice Similarity Coefficient (DSC) of 0.88 ± 0.02, IoU of 0.86 ± 0.03, and a Hausdorff Distance (HD) of 3.5 ± 0.8 mm. For glioma subtype classification, the model reached 97.5 % accuracy, 98.9 % precision, 98.8 % recall, and an F1-score of 98.85 %, outperforming benchmark methods such as U-Net, DeepSemantic, and AdaBoost. These results suggest that SEMD-Net effectively balances segmentation accuracy and classification robustness, offering a promising solution for integrated brain tumor analysis. While further validation on external datasets is ongoing, the proposed framework shows strong potential for clinical application in automated MRI-based diagnosis.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 132831"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New integral inequalities for exponential stability of neural networks with time-varying delay 时变时滞神经网络指数稳定性的新积分不等式

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-17 DOI: 10.1016/j.neucom.2026.133078

Han Xue , Yuanyuan Zhang , Chenyang Shi , Seakweng Vong

引用次数: 0

Gated X-TFC: Soft domain decomposition for forward and inverse problems in sharp-gradient PDEs 门控X-TFC：急梯度偏微分方程正逆问题的软域分解

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.neucom.2026.133090

Vikas Dwivedi , Enrico Schiassi , Bruno Sixou , Monica Sigovan

{"title":"Gated X-TFC: Soft domain decomposition for forward and inverse problems in sharp-gradient PDEs","authors":"Vikas Dwivedi , Enrico Schiassi , Bruno Sixou , Monica Sigovan","doi":"10.1016/j.neucom.2026.133090","DOIUrl":"10.1016/j.neucom.2026.133090","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) and related methods struggle to resolve sharp gradients in singularly perturbed boundary value problems without resorting to some form of domain decomposition, which often introduces complex interface penalties. While the Extreme Theory of Functional Connections (X-TFC), thanks to the TFC component, avoids multi-objective optimization by employing exact boundary condition enforcement, it remains computationally inefficient for boundary layers and incompatible with decomposition. We propose Gated X-TFC, a novel framework for both forward and inverse problems, that overcomes these limitations through a soft, learned domain decomposition. Our method replaces hard interfaces with a differentiable logistic gate that dynamically adapts radial basis function (RBF) kernel widths across the domain, eliminating the need for interface penalties. This approach yields not only superior accuracy but also significant improvements in computational efficiency: on a benchmark one dimensional (1D) convection–diffusion, Gated X-TFC achieves an order-of-magnitude lower error than standard X-TFC while using 80% fewer collocation points and reducing training time by 66%. In addition, we introduce an operator-conditioned meta-learning layer that learns a probabilistic mapping from PDE parameters to optimal gate configurations, enabling fast, uncertainty-aware warm-starting for new problem instances. We further demonstrate extensibility to multiple subdomains and higher dimensions by solving a twin boundary–layer equation and a 2D Poisson problem with a sharp Gaussian source. Overall, Gated X-TFC delivers a simple alternative to PINNs that is both accurate and computationally efficient for challenging boundary-layer regimes. Future work will focus on nonlinear problems. For reproducibility, all the codes are available at <span><span>https://github.com/vikas-dwivedi-2022/gated_xtfc</span><svg><path></path></svg></span></div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133090"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0