Neurocomputing最新文献_第9页

Distributed approximate aggregative optimization of multiple Euler–Lagrange systems using only sampling measurements

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.130000

Cong Li, Qingling Wang

引用次数: 0

A memory failure computational model in Alzheimer-like disease via continuous delayed Hopfield network with Lurie control system based healing

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129967

Rafael Fernandes Pinheiro , Diego Colón , Rui Fonseca-Pinto

{"title":"A memory failure computational model in Alzheimer-like disease via continuous delayed Hopfield network with Lurie control system based healing","authors":"Rafael Fernandes Pinheiro , Diego Colón , Rui Fonseca-Pinto","doi":"10.1016/j.neucom.2025.129967","DOIUrl":"10.1016/j.neucom.2025.129967","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a degenerative neurological condition that impacts millions of individuals across the globe and remains without a healing. In the search for new possibilities of treatments for this terrible disease, this work presents the improved Alzheimer-like disease (IALD) model for memory failure and connects it to a new control technique that establishes a cure for the memory lost, either in biological or in artificial neural networks. For the IALD model, continuous Hopfield neural networks (HNN) with time delay are used. From the healing side, a robust control technique is used, which is based on new discoveries in Lurie control systems. In addition, this paper reviews the development of Alzheimer-like disease (ALD) model, as well as, the relationship of HNN with Lurie system. Simulations are executed to validate the model and to show the efficacy of applying a new theorem from Lurie problem. With the results presented, this work proposes a new conceptual paradigm that could potentially be applied in memory failure treatments in AD, as well as in hardware implemented HNN under adversarial attacks or adverse environmental conditions.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"636 ","pages":"Article 129967"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687112","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

CITAL: Counterfactual intervention for temporal action localization with point-level annotation

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.130006

Yongxiang Hu , Ziying Xia , Zichong Chen , Thupten Tsering , Jian Cheng , Tashi Nyima

{"title":"CITAL: Counterfactual intervention for temporal action localization with point-level annotation","authors":"Yongxiang Hu , Ziying Xia , Zichong Chen , Thupten Tsering , Jian Cheng , Tashi Nyima","doi":"10.1016/j.neucom.2025.130006","DOIUrl":"10.1016/j.neucom.2025.130006","url":null,"abstract":"<div><div>Point-supervised temporal action localization (PTAL) requires only a timestamp annotated on each action instance for training. Most existing PTAL methods use multiple instances learning (MIL) paradigm that localize actions according to contributions of the snippets to the classification results. The gap between classification and localization tasks causes the models to focus more on clues than pure actions. And the models are prone to localize fake actions when there are biased clues between training and test datasets. Inspired by earlier efforts on causal inference, we propose a counterfactual intervention framework for PTAL, CITAL for short. Counterfactual intervention simulates how models respond to counterfactual inputs that contains the same clues without action instances. Intuitively, we can obtain the real response to the pure actions by comparing responses to the inputs before and after counterfactual intervention. Specifically, we propose a background suppression (BS) block to suppresses the background response by guiding the model pay more attention to action instances rather than clues. To fuse the output scores of the various inputs, we propose a fusing by imitation (FI) strategy that further modifies the scores to have a high response to actions and low response to the background segments, generating more accurate proposals. Besides, we propose a counterfactual example generation (CEG) block to generate counterfactual examples with only clues and background contents based on the point labels and snippet-level action scores. Our approach achieves significant mAP gains on THUMOS14, BEOID and GTEA benchmarks comparing to various CAS-based methods without introducing additional parameters.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"637 ","pages":"Article 130006"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706300","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

DPEC: Dual-Path Error Compensation for low-light image enhancement

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129980

Shuang Wang , Qianwen Lu , Boxing Peng , Yihe Nie , Qingchuan Tao

{"title":"DPEC: Dual-Path Error Compensation for low-light image enhancement","authors":"Shuang Wang , Qianwen Lu , Boxing Peng , Yihe Nie , Qingchuan Tao","doi":"10.1016/j.neucom.2025.129980","DOIUrl":"10.1016/j.neucom.2025.129980","url":null,"abstract":"<div><div>For the task of low-light image enhancement, deep learning-based algorithms have demonstrated superiority and effectiveness compared to traditional methods. However, these methods, primarily based on Retinex theory, tend to overlook the noise and color distortions in input images, leading to significant noise amplification and local color distortions in enhanced results. To address these issues, we propose the Dual-Path Error Compensation (DPEC) method, designed to improve image quality under low-light conditions by preserving local texture details while restoring global image brightness without amplifying noise. DPEC incorporates precise pixel-level error estimation to capture subtle differences and an independent denoising mechanism to prevent noise amplification. We introduce the HIS-Retinex loss to guide DPEC’s training, ensuring the brightness distribution of enhanced images closely aligns with real-world conditions. To balance computational speed and resource efficiency while training DPEC for a comprehensive understanding of the global context, we integrated the VMamba architecture into its backbone. Comprehensive quantitative and qualitative experimental results demonstrate that our algorithm significantly outperforms state-of-the-art methods in low-light image enhancement. The code is publicly available online at <span><span>https://github.com/wangshuang233/DPEC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"637 ","pages":"Article 129980"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715504","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

Optimized inverse dead-zone formation control using reinforcement learning for the nonlinear single-integrator dynamic multi-agent system

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129981

Guoxing Wen , Wenxia Sun , Shuaihua Ma

{"title":"Optimized inverse dead-zone formation control using reinforcement learning for the nonlinear single-integrator dynamic multi-agent system","authors":"Guoxing Wen , Wenxia Sun , Shuaihua Ma","doi":"10.1016/j.neucom.2025.129981","DOIUrl":"10.1016/j.neucom.2025.129981","url":null,"abstract":"<div><div>In this article, an optimized inverse dead-zone formation control using identifier–critic–actor reinforcement learning (RL) is studied for the nonlinear single-integral dynamic multi-agent system (MAS). Since MAS formation is often accompanied with a high energy expenditure, it is very necessary and essential to take optimization as a control design principle. In order to smoothly achieve the optimized MAS formation control, a simplified RL is developed by performing the gradient descent method to a simple positive function, which is equivalent to Hamilton–Jacobi–Bellman (HJB) equation. Furthermore, since the MAS formation cooperation is depended on the information exchange among agents, it is very possible to happen the control dead-zone phenomenon, which makes the actuator without control signal. For eliminating the effect of dead-zone, an adaptive inverse dead-zone method is developed and then is combined with RL for this optimized formation control. In comparison to the conventional inverse dead-zone approach, this design has the less adaptive parameters. Finally, the results of theoretical and simulation demonstrate viability of the proposed method.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"636 ","pages":"Article 129981"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686891","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

HSACT: A hierarchical semantic-aware CNN-Transformer for remote sensing image spectral super-resolution

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129990

Chengle Zhou , Zhi He , Liwei Zou , Yunfei Li , Antonio Plaza

{"title":"HSACT: A hierarchical semantic-aware CNN-Transformer for remote sensing image spectral super-resolution","authors":"Chengle Zhou , Zhi He , Liwei Zou , Yunfei Li , Antonio Plaza","doi":"10.1016/j.neucom.2025.129990","DOIUrl":"10.1016/j.neucom.2025.129990","url":null,"abstract":"<div><div>Hyperspectral remote sensing technology has demonstrated its spectral diagnosis advantages in numerous remote sensing observation fields. However, hyperspectral imaging is expensive and less portable compared to RGB imaging. To recover the corresponding hyperspectral image (HSI) from a remote sensing RGB image, this paper proposes a new hierarchical semantic-aware convolutional neural network (CNN)-Transformer (HSACT) for remote sensing image spectral super-resolution (SSR). Particularly, this work aims to reconstruct HSIs from RGB images within the same field of view using a lightweight semantic embedding architecture. Our HSACT consists of the following steps. First, an initial spectrum estimation module (from the RGB image to the HSI) is designed to progressively consider spectral estimation between RGB wavelength-inner and wavelength-outer information. Then, an attention-driven semantic-aware CNN-Transformer is developed to reconstruct the spatial and spectral details of HSI. Specifically, a trainable polymorphic superpixel convolution (PSConv) is proposed to capture features efficiently in the above module. Next, we introduce an information-lossless hierarchical network architecture to link the above modules and achieve end-to-end RGB image SSR through weight sharing. Experimental results on several datasets demonstrated that our HSACT outperforms traditional and advanced SSR methods. The codes of this paper are available from <span><span>https://github.com/chengle-zhou/HSACT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"636 ","pages":"Article 129990"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686893","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

YOLO-ELWNet: A lightweight object detection network

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129904

Baoye Song , Jianyu Chen , Weibo Liu , Jingzhong Fang , Yani Xue , Xiaohui Liu

引用次数: 0

Spatial–spectral morphological mamba for hyperspectral image classification

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129995

Muhammad Ahmad , Muhammad Hassaan Farooq Butt , Adil Mehmood Khan , Manuel Mazzara , Salvatore Distefano , Muhammad Usama , Swalpa Kumar Roy , Jocelyn Chanussot , Danfeng Hong

{"title":"Spatial–spectral morphological mamba for hyperspectral image classification","authors":"Muhammad Ahmad , Muhammad Hassaan Farooq Butt , Adil Mehmood Khan , Manuel Mazzara , Salvatore Distefano , Muhammad Usama , Swalpa Kumar Roy , Jocelyn Chanussot , Danfeng Hong","doi":"10.1016/j.neucom.2025.129995","DOIUrl":"10.1016/j.neucom.2025.129995","url":null,"abstract":"<div><div>Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often have inefficiencies, as their computational complexity scales quadratically with sequence length. To address these challenges, we propose the morphological spatial mamba (SMM) and morphological spatial–spectral Mamba (SSMM) model (MorpMamba), which combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers. In MorpMamba, a novel token generation module first converts HSI patches into spatial–spectral tokens. These tokens are then processed through morphological operations such as erosion and dilation, utilizing depthwise separable convolutions to capture structural and shape information. A token enhancement module refines these features by dynamically adjusting the spatial and spectral tokens based on central HSI regions, ensuring effective feature fusion within each block. Subsequently, multi-head self-attention is applied to enrich the feature representations further, allowing the model to capture complex relationships and dependencies within the data. Finally, the enhanced tokens are fed into a state space module, which efficiently models the temporal evolution of the features for classification. Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models while maintaining high accuracy. The source code is available at <span><span>https://github.com/mahmad000/MorpMamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"636 ","pages":"Article 129995"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695957","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

HDCPAA: A few-shot class-incremental learning model for remote sensing image recognition

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.130043

Peng Li, Cunqian Feng, Xiaowei Hu, Weike Feng

{"title":"HDCPAA: A few-shot class-incremental learning model for remote sensing image recognition","authors":"Peng Li, Cunqian Feng, Xiaowei Hu, Weike Feng","doi":"10.1016/j.neucom.2025.130043","DOIUrl":"10.1016/j.neucom.2025.130043","url":null,"abstract":"<div><div>In the scene of remote sensing image (RSI) recognition, it is difficult to obtain a sufficient number of samples for training all categories at once. A more realistic situation is that the recognition task occurs in an open environment, with categories gradually increasing. Additionally, due to the difficulty of collecting certain data, there are only a few samples for each new category. This leads to the problem of few-shot class-incremental learning (FSCIL), where the model learns incrementally and the number of samples for incremental classes is very small, generally only a few, while the number of samples for base classes is relatively large. To address this, this paper proposes a model framework for FSCIL of RSIs, called HDCPAA. The model is mainly divided into three parts. The first part is the feature extraction network, which is pre-trained on the base classes and then its parameters are frozen in subsequent incremental learning to alleviate catastrophic forgetting of the base classes. The second part is a fully connected layer, which transforms the prototypes of each category into quasi-orthogonal prototypes to increase the distance between the prototypes. The third part is the prototype adaptation attention module, which adaptively updates prototypes and query vectors using attention mechanisms. The training process of this module is based on the meta-learning of pseudo-incremental classes. Experiments on two popular benchmark RSI datasets, MSTAR and NWPU-RESISC45, show that our model significantly outperforms the baseline models and sets new state-of-the-art results with remarkable advantages. Our code will be uploaded at: <span><span>https://github.com/lipeng144/HDCPAA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"636 ","pages":"Article 130043"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rad-Mark: Reliable adversarial zero-watermarking

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-03-19 DOI: 10.1016/j.neucom.2025.129970

Kun Hu , Dakai Zhai , Heng Gao , Haoyu Xie , Xingjun Wang

{"title":"Rad-Mark: Reliable adversarial zero-watermarking","authors":"Kun Hu , Dakai Zhai , Heng Gao , Haoyu Xie , Xingjun Wang","doi":"10.1016/j.neucom.2025.129970","DOIUrl":"10.1016/j.neucom.2025.129970","url":null,"abstract":"<div><div>Zero-watermarking is a lossless protection technique, and thus it is widely used in medical images, artworks and other carriers that require lossless protection. However, the current zero-watermarking suffers from the problem of high similarity between the feature images of different host images, which results in a high false positive rate. To address this challenge, we propose Rad-Mark, a deep learning-based zero-watermarking framework that leverages adversarial feature optimization to enhance the robustness and accuracy of watermark detection significantly for the first time. The adversarial samples are employed to significantly improve the framework’s security, which can achieve the NC value of false positives close to 0.5. Both image perturbation and Gaussian noise are incorporated into the training process. Specifically, our Rad-Mark involves a feature fusion design, a mapping network based on the fusion of locally filtered and global handcrafted features. We conduct an in-depth analysis of key parameters, including Gaussian noise, watermark dimensions, and weighting factors, exploring their impact on the performance of our Rad-Mark. Extensive experimental results demonstrate that Rad-Mark outperforms existing zero-watermarking methods in terms of both security and robustness.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"636 ","pages":"Article 129970"},"PeriodicalIF":5.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687101","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