Neural Networks最新文献

Multi-level feature fusion networks for smoke recognition in remote sensing imagery. 多尺度特征融合网络用于遥感图像烟雾识别。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2025-01-04 DOI: 10.1016/j.neunet.2024.107112

Yupeng Wang, Yongli Wang, Zaki Ahmad Khan, Anqi Huang, Jianghui Sang

{"title":"Multi-level feature fusion networks for smoke recognition in remote sensing imagery.","authors":"Yupeng Wang, Yongli Wang, Zaki Ahmad Khan, Anqi Huang, Jianghui Sang","doi":"10.1016/j.neunet.2024.107112","DOIUrl":"10.1016/j.neunet.2024.107112","url":null,"abstract":"<p><p>Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke identification in remote sensing images is vital for effective forest fire monitoring within Internet of Things (IoT) systems. However, existing detection methods frequently falter in complex real-world scenarios, where variable smoke shapes and sizes, intricate backgrounds, and smoke-like phenomena (e.g., clouds and haze) lead to missed detections and false alarms. To address these challenges, we propose the Multi-level Feature Fusion Network (MFFNet), a novel framework grounded in contrastive learning. MFFNet begins by extracting multi-scale features from remote sensing images using a pre-trained ConvNeXt model, capturing information across different levels of granularity to accommodate variations in smoke appearance. The Attention Feature Enhancement Module further refines these multi-scale features, enhancing fine-grained, discriminative attributes relevant to smoke detection. Subsequently, the Bilinear Feature Fusion Module combines these enriched features, effectively reducing background interference and improving the model's ability to distinguish smoke from visually similar phenomena. Finally, contrastive feature learning is employed to improve robustness against intra-class variations by focusing on unique regions within the smoke patterns. Evaluated on the benchmark dataset USTC_SmokeRS, MFFNet achieves an accuracy of 98.87%. Additionally, our model demonstrates a detection rate of 94.54% on the extended E_SmokeRS dataset, with a low false alarm rate of 3.30%. These results highlight the effectiveness of MFFNet in recognizing smoke in remote sensing images, surpassing existing methodologies. The code is accessible at https://github.com/WangYuPeng1/MFFNet.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107112"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism. ICH-PRNet：基于联合注意相互作用机制的跨模式脑出血预后预测方法。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2025-01-06 DOI: 10.1016/j.neunet.2024.107096

Xinlei Yu, Ahmed Elazab, Ruiquan Ge, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qing Wu, Xiang Wan, Lihua Li, Changmiao Wang

{"title":"ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.","authors":"Xinlei Yu, Ahmed Elazab, Ruiquan Ge, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qing Wu, Xiang Wan, Lihua Li, Changmiao Wang","doi":"10.1016/j.neunet.2024.107096","DOIUrl":"10.1016/j.neunet.2024.107096","url":null,"abstract":"<p><p>Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH. On the other hand, existing cross-modal approaches that incorporate tabular data have often failed to effectively extract complementary information and cross-modal features between modalities, thereby limiting their prognostic capabilities. This study introduces a novel cross-modal network, ICH-PRNet, designed to predict ICH prognosis outcomes. Specifically, we propose a joint-attention interaction encoder that effectively integrates computed tomography images and clinical texts within a unified representational space. Additionally, we define a multi-loss function comprising three components to comprehensively optimize cross-modal fusion capabilities. To balance the training process, we employ a self-adaptive dynamic prioritization algorithm that adjusts the weights of each component, accordingly. Our model, through these innovative designs, establishes robust semantic connections between modalities and uncovers rich, complementary cross-modal information, thereby achieving superior prediction results. Extensive experimental results and comparisons with state-of-the-art methods on both in-house and publicly available datasets unequivocally demonstrate the superiority and efficacy of the proposed method. Our code is at https://github.com/YU-deep/ICH-PRNet.git.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107096"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Identity Model Transformation for boosting performance and efficiency in object detection network. 身份模型转换提高目标检测网络的性能和效率。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2024-12-31 DOI: 10.1016/j.neunet.2024.107098

Zhongyuan Lu, Jin Liu, Miaozhong Xu

{"title":"Identity Model Transformation for boosting performance and efficiency in object detection network.","authors":"Zhongyuan Lu, Jin Liu, Miaozhong Xu","doi":"10.1016/j.neunet.2024.107098","DOIUrl":"10.1016/j.neunet.2024.107098","url":null,"abstract":"<p><p>Modifying the structure of an existing network is a common method to further improve the performance of the network. However, modifying some layers in network often results in pre-trained weight mismatch, and fine-tune process is time-consuming and resource-inefficient. To address this issue, we propose a novel technique called Identity Model Transformation (IMT), which keep the output before and after transformation in an equal form by rigorous algebraic transformations. This approach ensures the preservation of the original model's performance when modifying layers. Additionally, IMT significantly reduces the total training time required to achieve optimal results while further enhancing network performance. IMT has established a bridge for rapid transformation between model architectures, enabling a model to quickly perform analytic continuation and derive a family of tree-like models with better performance. This model family possesses a greater potential for optimization improvements compared to a single model. Extensive experiments across various object detection tasks validated the effectiveness and efficiency of our proposed IMT solution, which saved 94.76% time in fine-tuning the basic model YOLOv4-Rot on DOTA 1.5 dataset, and by using the IMT method, we saw stable performance improvements of 9.89%, 6.94%, 2.36%, and 4.86% on the four datasets: AI-TOD, DOTA1.5, coco2017, and MRSAText, respectively.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107098"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Synergistic learning with multi-task DeepONet for efficient PDE problem solving. 协同学习与多任务DeepONet的高效PDE问题求解。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2025-01-03 DOI: 10.1016/j.neunet.2024.107113

Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D Shields, George Em Karniadakis

{"title":"Synergistic learning with multi-task DeepONet for efficient PDE problem solving.","authors":"Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D Shields, George Em Karniadakis","doi":"10.1016/j.neunet.2024.107113","DOIUrl":"10.1016/j.neunet.2024.107113","url":null,"abstract":"<p><p>Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107113"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network. 基于归算和社会感知图卷积神经网络的推荐系统增强。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2024-12-31 DOI: 10.1016/j.neunet.2024.107071

Azadeh Faroughi, Parham Moradi, Mahdi Jalili

{"title":"Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network.","authors":"Azadeh Faroughi, Parham Moradi, Mahdi Jalili","doi":"10.1016/j.neunet.2024.107071","DOIUrl":"10.1016/j.neunet.2024.107071","url":null,"abstract":"<p><p>Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in a sparse matrix. This inherent sparsity poses a challenge because it necessitates accurately and effectively filling in these gaps to provide users with meaningful and personalized recommendations. Our solution addresses sparsity in recommendations by incorporating diverse data sources, including trust statements and an imputation graph. The trust graph captures user relationships and trust levels, working in conjunction with an imputation graph, which is constructed by estimating the missing rates of each user based on the user-item matrix using the average rates of the most similar users. Combined with the user-item rating graph, an attention mechanism fine tunes the influence of these graphs, resulting in more personalized and effective recommendations. Our method consistently outperforms state-of-the-art recommenders in real-world dataset evaluations, underscoring its potential to strengthen recommendation systems and mitigate sparsity challenges.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107071"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Open-world semi-supervised relation extraction

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-02-20 DOI: 10.1016/j.neunet.2025.107266

Diange Zhou , Yilin Duan , Shengwen Li , Hong Yao

{"title":"Open-world semi-supervised relation extraction","authors":"Diange Zhou , Yilin Duan , Shengwen Li , Hong Yao","doi":"10.1016/j.neunet.2025.107266","DOIUrl":"10.1016/j.neunet.2025.107266","url":null,"abstract":"<div><div>Semi-supervised Relation Extraction methods play an important role in extracting relationships from unstructured text, which can leverage both labeled and unlabeled data to improve extraction accuracy. However, these methods are grounded under the closed-world assumption, in which the relationship types of labeled and unlabeled data belong to the same closed set, that are not applicable to real-world scenarios that involve novel relationships. To address this issue, this paper proposes an open-world semi-supervised relation extraction task and a novel method, Seen relation Identification and Novel relation Discovery (SIND), to extract both seen and novel relations simultaneously. Specifically, SIND develops a contrastive learning strategy to improve the semantic representation of relations and incorporates a cluster-aware method for discovering novel relations by leveraging the pairwise similarity between samples in the feature space. Additionally, SIND utilizes the maximum entropy theory as the prior distribution to address the learning pace imbalance problem caused by the absence of labeled data for novel classes. Experimental results on three widely used benchmark datasets demonstrate that SIND achieves significant improvements over baseline models. This study provides an exploration to address the challenge of discovering relationships within unannotated data and presents a reference approach for various natural language processing tasks, such as text classification and named entity recognition, in open-world scenarios. The datasets and source code of this work are available at <span><span>https://github.com/a-home-bird/SIND</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"186 ","pages":"Article 107266"},"PeriodicalIF":6.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-02-19 DOI: 10.1016/j.neunet.2025.107265

Zhaowei Wang , Jun Meng , Haibin Li , Qiguo Dai , Xiaohui Lin , Yushi Luan

{"title":"Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction","authors":"Zhaowei Wang , Jun Meng , Haibin Li , Qiguo Dai , Xiaohui Lin , Yushi Luan","doi":"10.1016/j.neunet.2025.107265","DOIUrl":"10.1016/j.neunet.2025.107265","url":null,"abstract":"<div><div>Accurate identification of molecular interactions is crucial for biological network analysis, which can provide valuable insights into fundamental regulatory mechanisms. Despite considerable progress driven by computational advancements, existing methods often rely on task-specific prior knowledge or inherent structural properties of molecules, which limits their generalizability and applicability. Recently, graph-based methods have emerged as a promising approach for predicting links in molecular networks. However, most of these methods focus primarily on aggregating topological information within individual domains, leading to an inadequate characterization of molecular interactions. To mitigate these challenges, we propose AMCGRL, a generalized multi-domain cooperative graph representation learning framework for multifarious molecular interaction prediction tasks. Concretely, AMCGRL incorporates multiple graph encoders to simultaneously learn molecular representations from both intra-domain and inter-domain graphs in a comprehensive manner. Then, the cross-domain decoder is employed to bridge these graph encoders to facilitate the extraction of task-relevant information across different domains. Furthermore, a hierarchical mutual attention mechanism is developed to capture complex pairwise interaction patterns between distinct types of molecules through inter-molecule communicative learning. Extensive experiments conducted on the various datasets demonstrate the superior representation learning capability of AMCGRL compared to the state-of-the-art methods, proving its effectiveness in advancing the prediction of molecular interactions.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"186 ","pages":"Article 107265"},"PeriodicalIF":6.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-02-19 DOI: 10.1016/j.neunet.2025.107268

Zhenghong Wang , Yi Wang , Furong Jia , Kun Liu , Yishan Zhang , Fan Zhang , Zhou Huang , Yu Liu

{"title":"Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources","authors":"Zhenghong Wang , Yi Wang , Furong Jia , Kun Liu , Yishan Zhang , Fan Zhang , Zhou Huang , Yu Liu","doi":"10.1016/j.neunet.2025.107268","DOIUrl":"10.1016/j.neunet.2025.107268","url":null,"abstract":"<div><div>Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation can lead to the waste of renewable energy. As renewable energy is becoming a larger part of the grid, it is crucial to balance generation across different resources due to the instability of renewable energy production, which depends on climate conditions. Long-term forecasting of electricity generation from multiple resources and regions can help achieve the balance and create sufficient buffers for targeted adjustments. This study revisits the cross-correlation among various energy sources across regions. Certain time-series within the grid that exhibit early fluctuations are identified as leading indicators for others. Based on the utilization of leading indicators, ALI-GC is proposed for the comprehensive modelling of global energy source interactions. Additionally, a novel deep learning model, ALI-GRU, is proposed for long-term (up to a month) collaborative electricity generation forecasting. We obtained regional-level hourly electricity generation data for the entire U.S. spanning from 2018 to 2024. In the context of hourly end-to-end forecasting and online learning scenarios, our ALI-GRU consistently outperforms state-of-the-art models by up to 11.63%. Our work demonstrates strong adaptability in large-scale, real-time forecasting scenarios, providing practical benefits for improving renewable energy management and utilization practices.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"186 ","pages":"Article 107268"},"PeriodicalIF":6.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-02-15 DOI: 10.1016/j.neunet.2025.107263

Minhui Yu , Yuqi Fang , Yunbi Liu , Andrea C. Bozoki , Shifu Xiao , Ling Yue , Mingxia Liu

{"title":"Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline","authors":"Minhui Yu , Yuqi Fang , Yunbi Liu , Andrea C. Bozoki , Shifu Xiao , Ling Yue , Mingxia Liu","doi":"10.1016/j.neunet.2025.107263","DOIUrl":"10.1016/j.neunet.2025.107263","url":null,"abstract":"<div><div>While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two modalities remains a tricky problem due to their inherently distinctive properties. In addition, current studies often face the problem of small sample sizes and missing PET data due to factors such as patient withdrawal or low image quality. To this end, we propose a hybrid multi-modality multi-task learning (HM<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>L) framework with cross-domain knowledge transfer for forecasting trajectories of SCD progression. Our HM<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>L comprises (1) <em>missing PET imputation</em>, (2) <em>multi-modality feature extraction</em> for MRI and PET feature learning with a novel softmax-triplet constraint, (3) attention-based <em>multi-modality fusion</em> of MRI and PET features, and (4) <em>multi-task prediction</em> of category labels and clinical scores such as Mini-Mental State Examination (MMSE) and Geriatric Depression Scale (GDS). To handle problems with small sample sizes, a transfer learning strategy is developed to enable knowledge transfer from a relatively large scale dataset with MRI and PET from 795 subjects to two small-scale SCD cohorts with a total of 136 subjects. Experimental results indicate HM<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>L surpasses several state-of-the-art methods in jointly predicting category labels and clinical scores of subjective cognitive decline. Results show that the MMSE scores of SCD subjects who develop mild cognitive impairment during the 2-year/7-year follow-up are significantly lower than those of subjects who remain stable, while there exists a complex relationship between SCD progression with GDS.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"186 ","pages":"Article 107263"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Learning temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-02-15 DOI: 10.1016/j.neunet.2025.107210

Sathiyamoorthi Arthanari, Dinesh Elayaperumal, Young Hoon Joo

{"title":"Learning temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection","authors":"Sathiyamoorthi Arthanari, Dinesh Elayaperumal, Young Hoon Joo","doi":"10.1016/j.neunet.2025.107210","DOIUrl":"10.1016/j.neunet.2025.107210","url":null,"abstract":"<div><div>In recent years, deep correlation filters have demonstrated outstanding performance in robust object tracking. Nevertheless, the correlation filters encounter challenges in managing huge occlusion, target deviation, and background clutter due to the lack of effective utilization of previous target information. To overcome these issues, we propose a novel temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection. To do this, we first presented the adaptive channel selection approach, which efficiently handles target deviation by adaptively selecting suitable channels during the learning stage. In addition, the adaptive channel selection method allows for dynamic adjustments to the filter based on the unique characteristics of the target object. This adaptability enhances the tracker’s flexibility, making it well-suited for diverse tracking scenarios. Second, we propose the spatial-aware correlation filter with dynamic spatial constraints, which effectively reduces the filter response in the complex background region by distinguishing between the foreground and background regions in the response map. Hence, the target can be easily identified within the foreground region. Third, we designed a temporal regularization approach that improves the target accuracy when the case of large appearance variations. Additionally, this temporal regularization method considers the present and previous frames of the target region, which significantly enhances the tracking ability by utilizing historical information. Finally, we present a comprehensive experiments analysis of the OTB-2013, OTB-2015, TempleColor-128, UAV-123, UAVDT, and DTB-70 benchmark datasets to demonstrate the effectiveness of the proposed approach against the state-of-the-trackers.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"186 ","pages":"Article 107210"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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