Neural Networks最新文献_第5页

Gender-independent kinship verification network via fuzzy disentangling and multi-metric inference 基于模糊解缠和多度量推理的性别无关亲属关系验证网络

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-06 DOI: 10.1016/j.neunet.2026.108691

Lei Li , Quan Zhou , Shanshan Gao , Chaoran Cui , Zhaoqiang Xia

{"title":"Gender-independent kinship verification network via fuzzy disentangling and multi-metric inference","authors":"Lei Li , Quan Zhou , Shanshan Gao , Chaoran Cui , Zhaoqiang Xia","doi":"10.1016/j.neunet.2026.108691","DOIUrl":"10.1016/j.neunet.2026.108691","url":null,"abstract":"<div><div>Kinship verification aims to determine whether two individuals share a familial relationship based on facial information. Cross-gender relationships (i.e., Father-Daughter and Mother-Son) continue to face formidable challenges due to the diversity and uncertainty of genetic inheritance. Existing studies primarily focus on extracting robust features and measuring similarity, with limited attention given to the fuzziness of gender differences. To address this issue, this paper proposes a kinship verification framework based on a fuzzy neural network, which adaptively extracts gender-independent kinship features and handles relationship fuzziness to improve cross-gender verification performance. Specifically, the Swin Transformer, which has demonstrated excellent performance in facial analysis, is employed to extract initial features. A fuzzy neural network is then designed to disentangle gender and kinship features, with a gender recognition task introduced to further enhance this disentanglement and improve the gender independence of kinship features. Subsequently, a multi-metric fuzzy reasoning module is adopted to integrate kinship features, extract latent kinship cues, and leverage a contrastive loss function to effectively mine potential negative sample information, thereby significantly enhancing the model’s robustness. Experimental results on three publicly available datasets demonstrate that the proposed method achieves state-of-the-art performance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108691"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174307","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

On the inherent robustness of one-stage object detection against out-of-distribution data 针对非分布数据的单阶段目标检测的固有鲁棒性

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.neunet.2026.108683

Aitor Martinez-Seras , Javier Del Ser , Aitzol Olivares-Rad , Alain Andres , Pablo Garcia-Bringas

{"title":"On the inherent robustness of one-stage object detection against out-of-distribution data","authors":"Aitor Martinez-Seras , Javier Del Ser , Aitzol Olivares-Rad , Alain Andres , Pablo Garcia-Bringas","doi":"10.1016/j.neunet.2026.108683","DOIUrl":"10.1016/j.neunet.2026.108683","url":null,"abstract":"<div><div>Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown objects in image data, which leverages the features extracted by the model from each sample. Differently from other recent approaches in the literature, our proposal does not require retraining the object detector, thereby allowing for the use of pretrained models. Our proposed OoD detector exploits the application of supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. Furthermore, it utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion. Our experiments analyze the Pareto trade-off between the performance detecting known and unknown objects resulting from different algorithmic configurations and inference confidence thresholds. We also compare the performance of our proposed algorithm to that of logits-based post-hoc OoD methods, as well as possible fusion strategies. Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark. The obtained results verify that the performance of avant-garde post-hoc OoD detectors can be further improved when combined with our proposed algorithm.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108683"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174308","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

MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis MS-STFNN：基于fmri的多尺度时空融合神经网络抑郁症诊断

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-09 DOI: 10.1016/j.neunet.2026.108685

Mengni Zhou , Miaofeng Wang , Rongkun Mi , Yan Niu , Xiaohong Cui , Ran Gao , Yuxi Li , Xin Wen , Jie Xiang

{"title":"MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis","authors":"Mengni Zhou , Miaofeng Wang , Rongkun Mi , Yan Niu , Xiaohong Cui , Ran Gao , Yuxi Li , Xin Wen , Jie Xiang","doi":"10.1016/j.neunet.2026.108685","DOIUrl":"10.1016/j.neunet.2026.108685","url":null,"abstract":"<div><div>Major depressive disorder (MDD) is a common mental disorder. Current clinical diagnosis primarily relies on subjective scales, underscoring an urgent need to develop objective neuroimaging diagnostic methods. Brain network analysis and deep learning techniques based on functional magnetic resonance imaging (fMRI) have been proven effective in uncovering abnormal spatial patterns associated with depression. However, the temporal dynamics of brain activity are also equally critical for understanding the neural mechanisms of MDD. Despite this importance, existing studies still suffer from insufficient characterization of spatio-temporal information. This study proposes a novel multi-scale spatio-temporal fusion neural network (MS-STFNN) that captures multi-granularity spatial features of the brain from local to global levels, while incorporating dynamic functional connectivity (DFC) and raw fMRI sequences to characterize time-varying properties at multiple resolution levels. Finally, effective classification of different depression subtypes is achieved through multi-scale feature fusion (MSFF). The results show that the proposed model outperforms baseline models across multiple subtype classification tasks. Ablation study further demonstrates the effectiveness of multi-granularity spatial feature extraction, multi-resolution temporal representation, and the spatio-temporal fusion strategy. This study provides a reliable framework for the objective diagnosis of depression, addressing the limitations of traditional subjective assessments, while also offering new insights into the efficient utilization of spatio-temporal features in brain network analysis.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108685"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174598","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

Exploring financial sentiment analysis via fine-tuning large language model and attributed graph neural network 通过微调大语言模型和属性图神经网络探索金融情绪分析。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1016/j.neunet.2026.108620

Zongshen Mu , Yujie Wan , Yueting Zhuang , Jie Tan , Hong Cheng , Yueyang Wang

{"title":"Exploring financial sentiment analysis via fine-tuning large language model and attributed graph neural network","authors":"Zongshen Mu , Yujie Wan , Yueting Zhuang , Jie Tan , Hong Cheng , Yueyang Wang","doi":"10.1016/j.neunet.2026.108620","DOIUrl":"10.1016/j.neunet.2026.108620","url":null,"abstract":"<div><div>Financial sentiment analysis (FSA) refers to the task of classifying textual content into predefined sentiment categories to analyze their potential impacts on financial market fluctuations. However, directly applying these pre-trained LLMs to FSA still poses significant challenges. Existing approaches fail to align with domain-specific objectives and struggle to adapt to customized financial data schemas. Moreover, these LLMs predict the stock change primarily depending on its own information, failing to take into account cross-impact among relevant stocks. In this paper, we propose a novel framework that synergizes an LLM with a Graph Neural Network (GNN) to model stock price dynamics, leveraging stock sentiment signals extracted from financial news. Specifically, we employ the open-source Llama-3-8B model as the backbone, then enhance its sensitivity to financial sentiment patterns through supervised fine-tuning (SFT) and direct preference optimization (DPO) techniques. Leveraging the sentiment outputs from the fine-tuned LLM, we design a GNN to enhance stock representations and model cross-asset dependencies via two types of text-attributed graphs, which dynamically encode time-varying price correlations. Experiments on the Chinese A-share market demonstrate that financial sentiment significantly influences stock price variations. Our framework outperforms previous baselines and exhibits an average improvement of 50% in Sharpe ratio.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108620"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114716","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

Dynamic bidirectional data recomposition for efficient road garbage segmentation in semi-supervised learning 基于半监督学习的道路垃圾高效分割的动态双向数据重组。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.neunet.2026.108655

Suheng Peng , Jiacai Liao , Libo Cao

{"title":"Dynamic bidirectional data recomposition for efficient road garbage segmentation in semi-supervised learning","authors":"Suheng Peng , Jiacai Liao , Libo Cao","doi":"10.1016/j.neunet.2026.108655","DOIUrl":"10.1016/j.neunet.2026.108655","url":null,"abstract":"<div><div>Deep neural networks excel in road garbage segmentation but require costly pixel-level annotations. Balancing accuracy and annotation costs is a key bottleneck in urban garbage management. Semi-supervised learning (SSL) reduces the dependence on annotations by utilizing large amounts of unlabeled data. However, existing methods face a key challenge: under extreme annotation imbalance, the scarce labeled data often lacks diversity. This leads to repeated reuse during training, preventing full information exploitation and causing model performance stagnation. Specifically, we introduce the Dynamic Bidirectional Data Recomposition (DBDR) mechanism, which dynamically adjusts the bidirectional information interaction between labeled and unlabeled data to solve the problem of representation stagnation. Early training: The labeled data is integrated into the unlabeled data stream according to confidence levels, guiding the model to prioritize capturing and stabilizing basic semantic prototypes. Mid-training: A dynamic memory queue is constructed to quantify the evolution of model confidence states over time. We use dynamic thresholds and dual validation to trigger a reverse flow of knowledge from unlabeled to labeled supervision. This breaks local optima in the encoder and reshapes the semantic decision boundaries. DBDR can be integrated into any current mainstream SSL framework. On a real-world road garbage dataset, DBDR delivers a significant performance boost over all five state-of-the-art baseline models. Ablation experiments validate its key improvements in the segmentation of confusing targets (e.g., plastic, paper). This research provides an economically feasible solution for future smart city waste management technologies.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108655"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120838","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

HpMiX: A Disease ceRNA biomarker prediction framework driven by graph topology-constrained Mixup and hypergraph residual enhancement HpMiX：一个基于图拓扑约束混合和超图残差增强的疾病ceRNA生物标志物预测框架。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI: 10.1016/j.neunet.2026.108662

Xinfei Wang , Lan Huang , Yan Wang , Renchu Guan , Zhuhong You , Fengfeng Zhou , Yuqing Li , Yuan Fu

{"title":"HpMiX: A Disease ceRNA biomarker prediction framework driven by graph topology-constrained Mixup and hypergraph residual enhancement","authors":"Xinfei Wang , Lan Huang , Yan Wang , Renchu Guan , Zhuhong You , Fengfeng Zhou , Yuqing Li , Yuan Fu","doi":"10.1016/j.neunet.2026.108662","DOIUrl":"10.1016/j.neunet.2026.108662","url":null,"abstract":"<div><div>The competing endogenous RNA (ceRNA) regulatory network (CENA) plays a critical role in elucidating the molecular mechanisms of diseases. However, existing computational methods primarily focus on modeling local topological structures of biological networks, struggling to capture high-order regulatory relationships and global topological structures, thus limiting a deeper understanding of complex regulatory interactions.</div><div>To address this, we propose HpMiX, a Graph Topology-Constrained Mixup (GTCM) and hypergraph residual enhancement learning framework for the discovery of disease-related ceRNA biomarkers. This framework first constructs a CENA network encompassing multi-molecule associations, including miRNA, lncRNA, circRNA, and mRNA, and models higher-order regulatory relationships using K-hop hyperedges. Biologically meaningful initial features are then extracted from CENA via a multi-structure hypergraph weighted random walk method (MHWRW), integrating prior biological knowledge and regulatory information. Subsequently, graph topology-constrained Mixup and multi-head attention, combined with a residual hypergraph neural network, are employed to generate robust node embeddings with both local and global context, enabling the identification of potential disease-ceRNA biomarkers.</div><div>Prediction results across multiple disease biomarkers demonstrate that HpMiX significantly outperforms state-of-the-art methods, validating its effectiveness in biological regulatory network representation learning. Case studies further confirm that the framework can effectively identify differentially expressed ceRNAs in diseases, highlighting its potential as a tool for pre-screening high-probability disease biomarkers.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108662"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127083","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

Decomposition and transfer of individual Q-values for decision-making of multi-agent reinforcement learning with communication 基于沟通的多智能体强化学习决策中个体q值的分解与迁移。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neunet.2026.108678

Xiaopeng Xu, Yadong Zhao, Dong Wang

{"title":"Decomposition and transfer of individual Q-values for decision-making of multi-agent reinforcement learning with communication","authors":"Xiaopeng Xu, Yadong Zhao, Dong Wang","doi":"10.1016/j.neunet.2026.108678","DOIUrl":"10.1016/j.neunet.2026.108678","url":null,"abstract":"<div><div>In partially observable multi-agent tasks, communication modules need to be trained to supplement information for decision-making, and decision-making modules need to be trained to analyze received information. This leads to their coupling during the process of multi-agent reinforcement learning. To alleviate the difficulty from the coupling, two transfer-based frameworks are proposed. The Simple Transfer Framework converts a difficult reinforcement learning into an easy one and a supervised learning. The Decomposition and Transfer Framework further decomposes this supervised learning into two simpler ones, based on the decomposition of communication-based individual Q-values. They are decomposed into local individual Q-values and their deviations. Based on this framework, the mutual information neural estimation is introduced to train message generators independently. The proposed frameworks are verified by training two communication-based architectures. They outperform direct reinforcement learning algorithms across various team sizes tested in the Drone Fight. The independent training method effectively facilitates the transfer of the deviations.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108678"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167535","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

BAED: A new paradigm for few-shot graph learning with explanation in the loop 一个在循环中有解释的少镜头图学习的新范例。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neunet.2026.108673

Chao Chen , Xujia Li , Dongsheng Hong , Shanshan Lin , Xiangwen Liao , Chuanyi Liu , Lei Chen

{"title":"BAED: A new paradigm for few-shot graph learning with explanation in the loop","authors":"Chao Chen , Xujia Li , Dongsheng Hong , Shanshan Lin , Xiangwen Liao , Chuanyi Liu , Lei Chen","doi":"10.1016/j.neunet.2026.108673","DOIUrl":"10.1016/j.neunet.2026.108673","url":null,"abstract":"<div><div>The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient due to the extensive expert knowledge required to annotate graph data. In this context, Few-Shot Graph Learning (FSGL) approaches have been developed over the years. Through sophisticated neural architectures and customized training pipelines, these approaches enhance model adaptability to new label distributions. However, compromises in the model’s robustness and interpretability can result in overfitting to noise in labeled data and degraded performance. This paper introduces the first explanation-in-the-loop framework for the FSGL problem, called BAED. We novelly employ the belief propagation algorithm to facilitate label augmentation on graphs. Then, leveraging an auxiliary graph neural network and the gradient backpropagation method, our framework effectively extracts explanatory subgraphs surrounding target nodes. The final predictions are based on these informative subgraphs while mitigating the influence of redundant information from neighboring nodes. Extensive experiments on seven benchmark datasets demonstrate superior prediction accuracy, training efficiency, and explanation quality of BAED. As a pioneer, this work highlights the potential of the explanation-based research paradigm in FSGL.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108673"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158712","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

A punishment neural network-based acceleration-level joint drift-free scheme for solving constrained motion planning problem of redundant robotic manipulators 一种基于惩罚神经网络的冗余机器人关节无漂移约束运动规划方案。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.neunet.2026.108684

Zhijun Zhang, Xitong Gao, Jinjia Guo

{"title":"A punishment neural network-based acceleration-level joint drift-free scheme for solving constrained motion planning problem of redundant robotic manipulators","authors":"Zhijun Zhang, Xitong Gao, Jinjia Guo","doi":"10.1016/j.neunet.2026.108684","DOIUrl":"10.1016/j.neunet.2026.108684","url":null,"abstract":"<div><div>To solve the repetitive motion problem of redundant robotic manipulators, a punishment neural network-based acceleration-level joint drift-free (PNN-ALJDF) scheme is designed. Traditional joint physical limits constraints are fixed and lack margin. Thus, a novel joint acceleration time-varying constraint is considered in the PNN-ALJDF scheme to avoid the joint state exceeding the physical limits. In addition, to ensure that redundant robotic manipulators can periodically return to the initial pose, a joint drift-free criterion is designed. Furthermore, the joint drift-free criterion, kinematics equation and joint acceleration time-varying constraint are formulated globally as an acceleration-level joint drift-free (ALJDF) scheme by a time-varying quadratic programming approach. Then, the ALJDF scheme is solved by the designed punishment neural network. Thus, the proposed PNN-ALJDF scheme is composed of the ALJDF scheme and punishment neural network. Finally, the simulations demonstrate that the PNN-ALJDF scheme avoids joints from drifting, and the states of joints are all within the acceleration time-varying constraint. In addition, the proposed PNN-ALJDF has higher solution accuracy than the linear variational inequalities-based primal-dual neural network.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108684"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158685","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

C3Net: A cross-modal collaborative calibration of features for object detection using frames and events C3Net：使用帧和事件进行目标检测的跨模态特征协同校准

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-02 DOI: 10.1016/j.neunet.2026.108651

Yunhua Chen , Jinyu Zhong , Yihao Guo , Zequan Xie , Jinsheng Xiao , Pinghua Chen

{"title":"C3Net: A cross-modal collaborative calibration of features for object detection using frames and events","authors":"Yunhua Chen , Jinyu Zhong , Yihao Guo , Zequan Xie , Jinsheng Xiao , Pinghua Chen","doi":"10.1016/j.neunet.2026.108651","DOIUrl":"10.1016/j.neunet.2026.108651","url":null,"abstract":"<div><div>Object detection by fusing RGB frames and event streams is challenging due to their inherent heterogeneity and significant statistical disparities, which often lead to suboptimal fusion in existing methods. To address this, we introduce C3Net, a novel framework built upon a paradigm shift from direct feature merging to Collaborative Calibration. First, we propose an Adaptive Balancing Time Surface (ABTS) to generate motion-robust event representations by mitigating spatial inconsistencies caused by varying object velocities. Second, the core Cross-Modal Feature Collaborative Calibration Module (CM-FCCM) performs mutual calibration of RGB and event features across channel and spatial dimensions, reducing modality discrepancies before fusion; the calibrated features are then fed back to the respective backbones for enriched feature learning. Finally, an Adaptive Channel Fusion Module (ACFM) dynamically integrates the modalities based on channel-wise confidence. Extensive experiments on PKU-DAVIS-SOD, DSEC-MOD, and PKU-DDD17-CAR datasets demonstrate that C3Net achieves state-of-the-art performance, showcasing its superior ability to leverage the complementary strengths of frames and events.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108651"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174304","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