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Self-FAGCFN:Graph-Convolution Fusion Network Based on Feature Fusion and Self-Supervised Feature Alignment for Pneumonia and Tuberculosis Diagnosis 认领 引用 被引量：1
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作者 Junding Sun Wenhao Tang +5 位作者 Lei Zhao Chaosheng Tang Xiaosheng Wu Zhaozhao Xu Bin Pu Yudong Zhang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2012-2029,共18页
Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely us... Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications. 展开更多
关键词 Feature fusion Self-supervised feature alignment Convolutional neural networks Graph convolutional networks Class imbalance Feature-centroid fusion
Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion 认领 引用
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作者 Ke Li Xiaofeng Wang Hu Wang 《Computers, Materials & Continua》 SCIE EI 2024年第10期1391-1407,共17页
In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate... In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values. 展开更多
关键词 Federated learning data heterogeneity feature alignment decision fusion hierarchical optimization
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Advancing Sports Image Classification and Analysis:Effective Data Augmentation and Feature Alignment Strategies 认领 引用
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作者 Ping Liu Chao Zhao +2 位作者 Bin Zang Sifeng Wang Shigen Shen 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第1期577-589,共13页
Sport plays a crucial role in society,influencing physical health,entertainment,and community engagement.As artificial intelligence advances,the ability to classify sport images accurately becomes increasingly crucial... Sport plays a crucial role in society,influencing physical health,entertainment,and community engagement.As artificial intelligence advances,the ability to classify sport images accurately becomes increasingly crucial.Effective sport image classification enhances applications,such as performance analysis,athlete tracking,and fan engagement.Despite its significance,current methods face challenges due to limited labeled datasets and issues with feature misalignment.This paper introduces a novel Contrastive Language-Image Pre-training(CLIP)based framework specifically designed for sport image classification.By incorporating data augmentation techniques,the approach addresses data sparsity and enriches the diversity of image-text pairings,reducing the need for extensive manual annotation.Additionally,feature alignment strategies tackle text-image misalignment issues that affect classification accuracy.This approach fills a significant research gap and offers practical solutions to improve classification performance in sport image analysis.The results of extensive experiments validate the effectiveness of the framework,demonstrating its potential to advance sports analytics and contribute to more precise and scalable solutions in sport image classification. 展开更多
关键词 sport image classification sports analytics data augmentation feature alignment
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A Dual Stream Multimodal Alignment and Fusion Network for Classifying Short Videos 认领 引用
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作者 ZHOU Ming WANG Tong 《Journal of Donghua University(English Edition)》 CAS 2025年第1期88-95,共8页
Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and t... Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and the modality fusion approach tends to be too simple,often neglecting modality alignment before fusion.This research introduces a novel dual stream multimodal alignment and fusion network named DMAFNet for classifying short videos.The network uses two unimodal encoder modules to extract features within modalities and exploits a multimodal encoder module to learn interaction between modalities.To solve the modality alignment problem,contrastive learning is introduced between two unimodal encoder modules.Additionally,masked language modeling(MLM)and video text matching(VTM)auxiliary tasks are introduced to improve the interaction between video frames and text modalities through backpropagation of loss functions.Diverse experiments prove the efficiency of DMAFNet in multimodal video classification tasks.Compared with other two mainstream baselines,DMAFNet achieves the best results on the 2022 WeChat Big Data Challenge dataset. 展开更多
关键词 video classification multimodal fusion feature alignment
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Proxy-Based Embedding Alignment for RGB-Infrared Person Re-Identification 认领 引用
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作者 Zhaopeng Dou Yifan Sun +1 位作者 Yali Li Shengjin Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第3期1112-1124,共13页
RGB-Infrared person re-IDentification(re-ID)aims to match RGB and infrared(IR)images of the same person.However,the modality discrepancy between RGB and IR images poses a significant challenge for re-ID.To address thi... RGB-Infrared person re-IDentification(re-ID)aims to match RGB and infrared(IR)images of the same person.However,the modality discrepancy between RGB and IR images poses a significant challenge for re-ID.To address this issue,this paper proposes a Proxy-based Embedding Alignment(PEA)method to align the RGB and IR modalities in the embedding space.PEA introduces modality-specific identity proxies and leverages the sample-to-proxy relations to learn the model.Specifically,PEA focuses on three types of alignments:intra-modality alignment,inter-modality alignment,and cycle alignment.Intra-modality alignment aims to align sample features and proxies of the same identity within a modality.Inter-modality alignment aims to align sample features and proxies of the same identity across different modalities.Cycle alignment requires that a proxy is aligned with itself after tracing it along a cross-modality cycle(e.g.,IR→RGB→IR).By integrating these alignments into the training process,PEA effectively mitigates the impact of modality discrepancy and learns discriminative features across modalities.We conduct extensive experiments on several RGB-IR re-ID datasets,and the results show that PEA outperforms current state-of-the-art methods.Notably,on SYSU-MM01 dataset,PEA achieves 71.0%mAP under the multi-shot setting of the indoor-search protocol,surpassing the best-performing method by 7.2%. 展开更多
关键词 cross-modality person re-identification feature alignment cycle consistency metric learning
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FaSRnet:a feature and semantics refinement network for human pose estimation 认领 引用
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作者 Yuanhong ZHONG Qianfeng XU +2 位作者 Daidi ZHONG Xun YANG Shanshan WANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2024年第4期513-526,共14页
Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addre... Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue.Currently,most methods explore temporal consistency through refinements of the final heatmaps.The heatmaps contain the semantics information of key points,and can improve the detection quality to a certain extent.However,they are generated by features,and feature-level refinements are rarely considered.In this paper,we propose a human pose estimation framework with refinements at the feature and semantics levels.We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions.An attention mechanism is then used to fuse auxiliary features with current features.In terms of semantics,we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps.The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018,and the results demonstrate the effectiveness of our method. 展开更多
关键词 Human pose estimation Multi-frame refinement Heatmap and offset estimation Feature alignment Multi-person
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R3DG:Retrieve,Rank,and Reconstruction with Different Granularities for Multimodal Sentiment Analysis 认领 引用 被引量：1
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作者 Yan Zhuang Yanru Zhang +1 位作者 Jiawen Deng Fuji Ren 《Research》 SCIE EI CSCD 2026年第2期652-667,共16页
Multimodal sentiment analysis(MSA)aims to assess emotional states by integrating information from text,audio,and video.However,the heterogeneous nature of these modalities presents substantial challenges for accurate ... Multimodal sentiment analysis(MSA)aims to assess emotional states by integrating information from text,audio,and video.However,the heterogeneous nature of these modalities presents substantial challenges for accurate sentiment prediction.Existing approaches typically align pairs of modalities using attention mechanisms or contrastive learning,which are computationally expensive.Additionally,they often rely on a single granularity of alignment,either by averaging features over all time steps or aligning features at each individual time step.These approaches overlook the fact that emotional expression can vary across individuals and contexts,requiring multiple granularities to capture emotion effectively.To address these challenges,we propose a novel framework,Retrieve,Rank,and Reconstruction with Different Granularities(R3DG).R3DG segments the audio and video modalities into multiple representations at varying granularities based on their temporal durations.It then selects the most relevant representations that align closely with the text modality.To preserve the original information,R3DG reconstructs the audio and video data using the selected representations.Finally,the fused audio,video,and text features are aligned and combined for sentiment prediction,reducing the need for multiple alignment steps.Extensive experiments on 5 benchmark MSA datasets demonstrate that R3DG outperforms existing methods and achieves substantial reductions in computational time.Code is available at http://gffzz188fe103f8f1460asn0q6pp0w6noc6b6c.ffgz.tsg.suse.edu.cn/YetZzzzzz/R3DG. 展开更多
关键词 align pairs modalities contrastive learningwhich aligning featur integrating information attention mechanisms Multimodal Sentiment Analysis assess emotional states multimodal sentiment analysis msa aims
A Remote Sensing Image Fusion Network Based on EMA-FusionNet 认领 引用
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作者 Jiaxin Zheng Wendong Li 《Journal of Intelligent Science and Engineering Technology》 2026年第2期58-69,共12页
For addressing the problem of modality disparity between the MS and PAN Images and the spatial traits on remote sensing images,we introduce EMA-FusionNet,a novel Deep Fusion network which building on the strong ground... For addressing the problem of modality disparity between the MS and PAN Images and the spatial traits on remote sensing images,we introduce EMA-FusionNet,a novel Deep Fusion network which building on the strong groundwork of FusionCNN's.Our Multi-Scale Convolutional innovatively integrates EMA attention modules with various levels.It did better at feature extraction and fusion by adding an adaptive feature select,Cross-Modality.Therefore,it can capture more image information.The experimental results shows that EMA-FusionNet is always improving the level of fusion,and the best model improved by 4.76%which is effective and efficacy. 展开更多
关键词 Remote Sensing Image Fusion FusionCNN EMA Attention Mechanism Cross-Modal Fusion Feature Alignment
FAANet: feature-aligned attention network for real-time multiple object tracking in UAV videos 认领 引用 被引量：7
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作者 Zhenqi Liang Jingshi Wang +1 位作者 Gang Xiao Liu Zeng 《Chinese Optics Letters》 SCIE EI CAS CSCD 2022年第8期6-11,共6页
Multiple object tracking(MOT)in unmanned aerial vehicle(UAV)videos has attracted attention.Because of the observation perspectives of UAV,the object scale changes dramatically and is relatively small.Besides,most MOT ... Multiple object tracking(MOT)in unmanned aerial vehicle(UAV)videos has attracted attention.Because of the observation perspectives of UAV,the object scale changes dramatically and is relatively small.Besides,most MOT algorithms in UAV videos cannot achieve real-time due to the tracking-by-detection paradigm.We propose a feature-aligned attention network(FAANet).It mainly consists of a channel and spatial attention module and a feature-aligned aggregation module.We also improve the real-time performance using the joint-detection-embedding paradigm and structural re-parameterization technique.We validate the effectiveness with extensive experiments on UAV detection and tracking benchmark,achieving new state-of-the-art 44.0 MOTA,64.6 IDF1 with 38.24 frames per second running speed on a single 1080Ti graphics processing unit. 展开更多
关键词 multiple object tracking unmanned aerial vehicle feature alignment deep learning
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