[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature （MMP-GF）, is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved w...A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature （MMP-GF）, is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved with a family of Gabor kernels, and then according to the face structure and the key-points locations, the calculated Gabor images were divided into five parts： Gabor face, Gabor eyebrow, Gabor eye, Gabor nose and Gabor mouth. After that multi-modal Gabor features were spatially partitioned into non-overlapping regions and the averages of regions were concatenated to be a low dimension feature vector, whose dimension was further reduced by principal component analysis （PCA）. In the decision level fusion, match results respectively calculated based on the five parts were combined according to linear discriminant analysis （LDA） and a normalized matching algorithm was used to improve the performance. Experiments on FERET database show that the proposed MMP-GF method achieves good robustness to the expression and age variations.展开更多
The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-genera...The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.展开更多
In geometry processing,symmetry research benefits from global geo-metric features of complete shapes,but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resoluti...In geometry processing,symmetry research benefits from global geo-metric features of complete shapes,but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resolution,single viewpoint,and occlusion.Different from the existing works predicting symmetry from the complete shape,we propose a learning approach for symmetry predic-tion based on a single RGB-D image.Instead of directly predicting the symmetry from incomplete shapes,our method consists of two modules,i.e.,the multi-mod-al feature fusion module and the detection-by-reconstruction module.Firstly,we build a channel-transformer network(CTN)to extract cross-fusion features from the RGB-D as the multi-modal feature fusion module,which helps us aggregate features from the color and the depth separately.Then,our self-reconstruction net-work based on a 3D variational auto-encoder(3D-VAE)takes the global geo-metric features as input,followed by a prediction symmetry network to detect the symmetry.Our experiments are conducted on three public datasets:ShapeNet,YCB,and ScanNet,we demonstrate that our method can produce reliable and accurate results.展开更多
In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is pro...In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.展开更多
The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring ef...The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality rates.Detecting and treating ABE promptly is important to prevent further complications and long-ter...Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality rates.Detecting and treating ABE promptly is important to prevent further complications and long-term issues.Recent studies have explored ABE diagnosis.However,they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging(MRI).To tackle this problem,the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI scans.The scans include T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),and apparent diffusion coefficient maps to get indepth information.Initially,the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and Z-score normalisation for data standardisation.An Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection accuracy.Furthermore,a multi-transformer approach was used for feature fusion and identify feature correlations effectively.Finally,accurate ABE diagnosis is achieved through the utilisation of a SoftMax layer.The performance of the proposed Tri-M2MT model is evaluated across various metrics,including accuracy,specificity,sensitivity,F1-score,and ROC curve analysis,and the proposed methodology provides better performance compared to existing methodologies.展开更多
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.展开更多
The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which jus...The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which justifies the use of peer feedback in interpreting practice,the research methodology and data collection.Then it brings forth specific findings concerning the implementation of peer feedback in the interpreting class followed by discussions of the role and features of student peer feedback as a means to help students ready for the booth.Analysis of the results shows that peer feedback in interpreting practice keeps students on-task,attentive and help them spot their own problems.Trainers and students themselves point to similar features of student peer feedback as focusing on comprehension of the original,word choice and numbers.The preliminary findings of the survey demonstrate the roles and features of student peer feedback in interpreting practice and point to the possible way of enhancing student’s learning curve through more effective peer feedback.展开更多
For the analysis of spinal and disc diseases,automated tissue segmentation of the lumbar spine is vital.Due to the continuous and concentrated location of the target,the abundance of edge features,and individual diffe...For the analysis of spinal and disc diseases,automated tissue segmentation of the lumbar spine is vital.Due to the continuous and concentrated location of the target,the abundance of edge features,and individual differences,conventional automatic segmentation methods perform poorly.Since the success of deep learning in the segmentation of medical images has been shown in the past few years,it has been applied to this task in a number of ways.The multi-scale and multi-modal features of lumbar tissues,however,are rarely explored by methodologies of deep learning.Because of the inadequacies in medical images availability,it is crucial to effectively fuse various modes of data collection for model training to alleviate the problem of insufficient samples.In this paper,we propose a novel multi-modality hierarchical fusion network(MHFN)for improving lumbar spine segmentation by learning robust feature representations from multi-modality magnetic resonance images.An adaptive group fusion module(AGFM)is introduced in this paper to fuse features from various modes to extract cross-modality features that could be valuable.Furthermore,to combine features from low to high levels of cross-modality,we design a hierarchical fusion structure based on AGFM.Compared to the other feature fusion methods,AGFM is more effective based on experimental results on multi-modality MR images of the lumbar spine.To further enhance segmentation accuracy,we compare our network with baseline fusion structures.Compared to the baseline fusion structures(input-level:76.27%,layer-level:78.10%,decision-level:79.14%),our network was able to segment fractured vertebrae more accurately(85.05%).展开更多
How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance.To address this problem,this paper introduces a novel image retrieval mechanism based on the family fi...How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance.To address this problem,this paper introduces a novel image retrieval mechanism based on the family filtration in object region.First,we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object,retrieved from the corpus based on 100 images,as a result of the first rank.To further improve retrieval performance,we add an efficient spatial consistency stage,which is named family-based spatial consistency filtration,to re-rank the results returned by the first rank.We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of"TREC Video Retrieval Evaluation 2005(TRECVID2005)".The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality.The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.展开更多
Visual Question Answering(VQA)has sparked widespread interest as a crucial task in integrating vision and language.VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual r...Visual Question Answering(VQA)has sparked widespread interest as a crucial task in integrating vision and language.VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions.The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively.However,it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details,which is the advantage of grid-based features.In this paper,we propose a Dual-Level Feature Embedding(DLFE)network,which effectively integrates grid-based and detection-based image features in a unified architecture to realize the complementary advantages of both features.Specifically,in DLFE,In DLFE,firstly,a novel Dual-Level Self-Attention(DLSA)modular is proposed to mine the intrinsic properties of the two features,where Positional Relation Attention(PRA)is designed to model the position information.Then,we propose a Feature Fusion Attention(FFA)to address the semantic noise caused by the fusion of two features and construct an alignment graph to enhance and align the grid and detection features.Finally,we use co-attention to learn the interactive features of the image and question and answer questions more accurately.Our method has significantly improved compared to the baseline,increasing accuracy from 66.01%to 70.63%on the test-std dataset of VQA 1.0 and from 66.24%to 70.91%for the test-std dataset of VQA 2.0.展开更多
The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discr...The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A mo dified RSBRA for feature selection was proposed and evaluated with SVM classifie rs. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of th e RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accur acy and less training time than the original RSBRA.展开更多
Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s...Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.展开更多
Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of vi...Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.展开更多
Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplic...Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplicate detection methods. We have designed a coarse-to-fine near duplicate detection framework to speed-up the process and a multi-modal integra-tion scheme for accurate detection. The duplicate pairs are detected with both global feature (partition based color his-togram) and local feature (CPAM and SIFT Bag-of-Word model). The experiment results on large scale data set proved the effectiveness of the proposed design.展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金Supported by the National Key Technology R&D Program (No. 2006BAK08B07)
文摘A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature （MMP-GF）, is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved with a family of Gabor kernels, and then according to the face structure and the key-points locations, the calculated Gabor images were divided into five parts： Gabor face, Gabor eyebrow, Gabor eye, Gabor nose and Gabor mouth. After that multi-modal Gabor features were spatially partitioned into non-overlapping regions and the averages of regions were concatenated to be a low dimension feature vector, whose dimension was further reduced by principal component analysis （PCA）. In the decision level fusion, match results respectively calculated based on the five parts were combined according to linear discriminant analysis （LDA） and a normalized matching algorithm was used to improve the performance. Experiments on FERET database show that the proposed MMP-GF method achieves good robustness to the expression and age variations.
基金the National Natural Science Foundation of China(No.61976080)the Academic Degrees&Graduate Education Reform Project of Henan Province(No.2021SJGLX195Y)+1 种基金the Teaching Reform Research and Practice Project of Henan Undergraduate Universities(No.2022SYJXLX008)the Key Project on Research and Practice of Henan University Graduate Education and Teaching Reform(No.YJSJG2023XJ006)。
文摘The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.
文摘In geometry processing,symmetry research benefits from global geo-metric features of complete shapes,but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resolution,single viewpoint,and occlusion.Different from the existing works predicting symmetry from the complete shape,we propose a learning approach for symmetry predic-tion based on a single RGB-D image.Instead of directly predicting the symmetry from incomplete shapes,our method consists of two modules,i.e.,the multi-mod-al feature fusion module and the detection-by-reconstruction module.Firstly,we build a channel-transformer network(CTN)to extract cross-fusion features from the RGB-D as the multi-modal feature fusion module,which helps us aggregate features from the color and the depth separately.Then,our self-reconstruction net-work based on a 3D variational auto-encoder(3D-VAE)takes the global geo-metric features as input,followed by a prediction symmetry network to detect the symmetry.Our experiments are conducted on three public datasets:ShapeNet,YCB,and ScanNet,we demonstrate that our method can produce reliable and accurate results.
基金National Youth Natural Science Foundation of China(No.61806006)Innovation Program for Graduate of Jiangsu Province(No.KYLX160-781)Jiangsu University Superior Discipline Construction Project。
文摘In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071315 and 62271336).
文摘The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
文摘Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality rates.Detecting and treating ABE promptly is important to prevent further complications and long-term issues.Recent studies have explored ABE diagnosis.However,they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging(MRI).To tackle this problem,the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI scans.The scans include T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),and apparent diffusion coefficient maps to get indepth information.Initially,the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and Z-score normalisation for data standardisation.An Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection accuracy.Furthermore,a multi-transformer approach was used for feature fusion and identify feature correlations effectively.Finally,accurate ABE diagnosis is achieved through the utilisation of a SoftMax layer.The performance of the proposed Tri-M2MT model is evaluated across various metrics,including accuracy,specificity,sensitivity,F1-score,and ROC curve analysis,and the proposed methodology provides better performance compared to existing methodologies.
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
文摘The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which justifies the use of peer feedback in interpreting practice,the research methodology and data collection.Then it brings forth specific findings concerning the implementation of peer feedback in the interpreting class followed by discussions of the role and features of student peer feedback as a means to help students ready for the booth.Analysis of the results shows that peer feedback in interpreting practice keeps students on-task,attentive and help them spot their own problems.Trainers and students themselves point to similar features of student peer feedback as focusing on comprehension of the original,word choice and numbers.The preliminary findings of the survey demonstrate the roles and features of student peer feedback in interpreting practice and point to the possible way of enhancing student’s learning curve through more effective peer feedback.
基金supported in part by the Technology Innovation 2030 under Grant 2022ZD0211700.
文摘For the analysis of spinal and disc diseases,automated tissue segmentation of the lumbar spine is vital.Due to the continuous and concentrated location of the target,the abundance of edge features,and individual differences,conventional automatic segmentation methods perform poorly.Since the success of deep learning in the segmentation of medical images has been shown in the past few years,it has been applied to this task in a number of ways.The multi-scale and multi-modal features of lumbar tissues,however,are rarely explored by methodologies of deep learning.Because of the inadequacies in medical images availability,it is crucial to effectively fuse various modes of data collection for model training to alleviate the problem of insufficient samples.In this paper,we propose a novel multi-modality hierarchical fusion network(MHFN)for improving lumbar spine segmentation by learning robust feature representations from multi-modality magnetic resonance images.An adaptive group fusion module(AGFM)is introduced in this paper to fuse features from various modes to extract cross-modality features that could be valuable.Furthermore,to combine features from low to high levels of cross-modality,we design a hierarchical fusion structure based on AGFM.Compared to the other feature fusion methods,AGFM is more effective based on experimental results on multi-modality MR images of the lumbar spine.To further enhance segmentation accuracy,we compare our network with baseline fusion structures.Compared to the baseline fusion structures(input-level:76.27%,layer-level:78.10%,decision-level:79.14%),our network was able to segment fractured vertebrae more accurately(85.05%).
基金supported by National High Technology Research and Development Program of China(863 Program)(No.2007AA01Z416)National Natural Science Foundation of China(No.60773056)+1 种基金Beijing New Star Project on Science and Technology(No.2007B071)Natural Science Foundation of Liaoning Province of China(No.20052184)
文摘How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance.To address this problem,this paper introduces a novel image retrieval mechanism based on the family filtration in object region.First,we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object,retrieved from the corpus based on 100 images,as a result of the first rank.To further improve retrieval performance,we add an efficient spatial consistency stage,which is named family-based spatial consistency filtration,to re-rank the results returned by the first rank.We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of"TREC Video Retrieval Evaluation 2005(TRECVID2005)".The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality.The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.
文摘Visual Question Answering(VQA)has sparked widespread interest as a crucial task in integrating vision and language.VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions.The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively.However,it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details,which is the advantage of grid-based features.In this paper,we propose a Dual-Level Feature Embedding(DLFE)network,which effectively integrates grid-based and detection-based image features in a unified architecture to realize the complementary advantages of both features.Specifically,in DLFE,In DLFE,firstly,a novel Dual-Level Self-Attention(DLSA)modular is proposed to mine the intrinsic properties of the two features,where Positional Relation Attention(PRA)is designed to model the position information.Then,we propose a Feature Fusion Attention(FFA)to address the semantic noise caused by the fusion of two features and construct an alignment graph to enhance and align the grid and detection features.Finally,we use co-attention to learn the interactive features of the image and question and answer questions more accurately.Our method has significantly improved compared to the baseline,increasing accuracy from 66.01%to 70.63%on the test-std dataset of VQA 1.0 and from 66.24%to 70.91%for the test-std dataset of VQA 2.0.
基金National Key Fundamental Research Pro-ject of China (No.2002cb312200-01-3),National Natural Science Foundation ofChina (No.60174038) and Specialized Re-search Fund for the Doctoral Program ofHigher Education (No.20030248040)
文摘The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A mo dified RSBRA for feature selection was proposed and evaluated with SVM classifie rs. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of th e RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accur acy and less training time than the original RSBRA.
基金supported by the National Natural Science Foundation of China (Nos.62072127,62002076,61906049)Natural Science Foundation of Guangdong Province (Nos.2023A1515011774,2020A1515010423)+4 种基金Project 6142111180404 supported by CNKLSTISS,Science and Technology Program of Guangzhou,China (No.202002030131)Guangdong basic and applied basic research fund joint fund Youth Fund (No.2019A1515110213)Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No.MJUKF-IPIC202101)Natural Science Foundation of Guangdong Province No.2020A1515010423)Scientific research project for Guangzhou University (No.RP2022003).
文摘Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.
文摘Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.
文摘Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplicate detection methods. We have designed a coarse-to-fine near duplicate detection framework to speed-up the process and a multi-modal integra-tion scheme for accurate detection. The duplicate pairs are detected with both global feature (partition based color his-togram) and local feature (CPAM and SIFT Bag-of-Word model). The experiment results on large scale data set proved the effectiveness of the proposed design.
