Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems.Previous approaches have primarily utilized convolutional neural networks(CNNs)to effectively integrate spa...Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems.Previous approaches have primarily utilized convolutional neural networks(CNNs)to effectively integrate spatial and semantic information.However,the performance of CNN-based methods remains limited in classification accuracy,primarily due to insufficient exploration of local image characteristics.Unlike CNNs,Vision Transformer(ViT)captures discriminative features by modeling relationships between local image patches.However,such methods typically require a large number of training samples to perform effectively.In the context of foreign body classification on coal conveyor belts,the limited availability of training samples hinders the full exploitation of Vision Transformer’s(ViT)capabilities.To address this issue,we propose an efficient approach,termed Key Part-level Attention Vision Transformer(KPA-ViT),which incorporates key local information into the transformer architecture to enrich the training information.It comprises three main components:a key-point detection module,a key local mining module,and an attention module.To extract key local regions,a key-point detection strategy is first employed to identify the positions of key points.Subsequently,the key local mining module extracts the relevant local features based on these detected points.Finally,an attention module composed of self-attention and cross-attention blocks is introduced to integrate global and key part-level information,thereby enhancing the model’s ability to learn discriminative features.Compared to recent transformer-based frameworks—such as ViT,Swin-Transformer,and EfficientViT—the proposed KPA-ViT achieves performance improvements of 9.3%,6.6%,and 2.8%,respectively,on the CUMT-BelT dataset,demonstrating its effectiveness.展开更多
Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limit...Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limited receptive fields,making it difficult to capture global feature dependencies which is important for object detection,especially when the target undergoes large-scale variations or movement.In view of this,we develop a novel network called effective convolution mixed Transformer Siamese network(SiamCMT)for visual tracking,which integrates CNN-based and Transformer-based architectures to capture both local information and long-range dependencies.Specifically,we design a Transformer-based module named lightweight multi-head attention(LWMHA)which can be flexibly embedded into stage-wise CNNs and improve the network’s representation ability.Additionally,we introduce a stage-wise feature aggregation mechanism which integrates features learned from multiple stages.By leveraging both location and semantic information,this mechanism helps the SiamCMT to better locate and find the target.Moreover,to distinguish the contribution of different channels,a channel-wise attention mechanism is introduced to enhance the important channels and suppress the others.Extensive experiments on seven challenging benchmarks,i.e.,OTB2015,UAV123,GOT10K,LaSOT,DTB70,UAVTrack112_L,and VOT2018,demonstrate the effectiveness of the proposed algorithm.Specially,the proposed method outperforms the baseline by 3.5%and 3.1%in terms of precision and success rates with a real-time speed of 59.77 FPS on UAV123.展开更多
Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood s...Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood scenarios involving reflections,occlusions,or indistinct boundaries due to limited contextual modeling.To address these challenges,we propose a hybrid flood segmentation framework that integrates a Vision Transformer(ViT)encoder with a U-Net decoder,enhanced by a novel Flood-Aware Refinement Block(FARB).The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms.We evaluate our model on a UAV-acquired flood imagery dataset,demonstrating that the proposed ViTUNet+FARB architecture outperforms existing CNN and Transformer-based models in terms of accuracy and mean Intersection over Union(mIoU).Detailed ablation studies further validate the contribution of each component,confirming that the FARB design significantly enhances segmentation quality.To its better performance and computational efficiency,the proposed framework is well-suited for flood monitoring and disaster response applications,particularly in resource-constrained environments.展开更多
针对当前遥感农作物分类研究中深度学习模型对光谱时间和空间信息特征采样不足,农作物提取仍然存在边界模糊、漏提、误提的问题,提出了一种名为视觉Transformer-长短期记忆递归神经网络(Vision Transformer-long short term memory,ViTL...针对当前遥感农作物分类研究中深度学习模型对光谱时间和空间信息特征采样不足,农作物提取仍然存在边界模糊、漏提、误提的问题,提出了一种名为视觉Transformer-长短期记忆递归神经网络(Vision Transformer-long short term memory,ViTL)的深度学习模型,ViTL模型集成了双路Vision-Transformer特征提取、时空特征融合和长短期记忆递归神经网络(LSTM)时序分类等3个关键模块,双路Vision-Transformer特征提取模块用于捕获图像的时空特征相关性,一路提取空间分类特征,一路提取时间变化特征;时空特征融合模块用于将多时特征信息进行交叉融合;LSTM时序分类模块捕捉多时序的依赖关系并进行输出分类。综合利用基于多时序卫星影像的遥感技术理论和方法,对黑龙江省齐齐哈尔市讷河市作物信息进行提取,研究结果表明,ViTL模型表现出色,其总体准确率(Overall Accuracy,OA)、平均交并比(Mean Intersection over Union,MIoU)和F1分数分别达到0.8676、0.6987和0.8175,与其他广泛使用的深度学习方法相比,包括三维卷积神经网络(3-D CNN)、二维卷积神经网络(2-D CNN)和长短期记忆递归神经网络(LSTM),ViTL模型的F1分数提高了9%~12%,显示出显著的优越性。ViTL模型克服了面对多时序遥感影像的农作物分类任务中的时间和空间信息特征采样不足问题,为准确、高效地农作物分类提供了新思路。展开更多
It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of col...It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.展开更多
Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have becom...Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.展开更多
【目的】高分辨率遥感影像语义分割通过精准提取地物信息,为城市规划、土地分析利用提供了重要的数据支持。当前分割方法通常将遥感影像划分为标准块,进行多尺度局部分割和层次推理,未充分考虑影像中的上下文先验知识和局部特征交互能力...【目的】高分辨率遥感影像语义分割通过精准提取地物信息,为城市规划、土地分析利用提供了重要的数据支持。当前分割方法通常将遥感影像划分为标准块,进行多尺度局部分割和层次推理,未充分考虑影像中的上下文先验知识和局部特征交互能力,影响了推理分割质量。【方法】为了解决这一问题,本文提出了一种联合跨尺度注意力和语义视觉Transformer的遥感影像分割框架(Cross-scale Attention Transformer,CATrans),融合跨尺度注意力模块和语义视觉Transformer,提取上下文先验知识增强局部特征表示和分割性能。首先,跨尺度注意力模块通过空间和通道两个维度进行并行特征处理,分析浅层-深层和局部-全局特征之间的依赖关系,提升对遥感影像中不同粒度对象的注意力。其次,语义视觉Transformer通过空间注意力机制捕捉上下文语义信息,建模语义信息之间的依赖关系。【结果】本文在DeepGlobe、Inria Aerial和LoveDA数据集上进行对比实验,结果表明:CATrans的分割性能优于现有的WSDNet(Discrete Wavelet Smooth Network)和ISDNet(Integrating Shallow and Deep Network)等分割算法,分别取得了76.2%、79.2%、54.2%的平均交并比(Mean Intersection over Union,mIoU)和86.5%、87.8%、66.8%的平均F1得分(Mean F1 Score,mF1),推理速度分别达到38.1 FPS、13.2 FPS和95.22 FPS。相较于本文所对比的最佳方法WSDNet,mIoU和mF1在3个数据集中分别提升2.1%、4.0%、5.3%和1.3%、1.8%、5.6%,在每类地物的分割中都具有显著优势。【结论】本方法实现了高效率、高精度的高分辨率遥感影像语义分割。展开更多
基金funded by the National Key Research and Development Program of China(grant number 2023YFC2907600)the National Natural Science Foundation of China(grant number 52504132)Tiandi Science and Technology Co.,Ltd.Science and Technology Innovation Venture Capital Special Project(grant number 2023-TD-ZD011-004).
文摘Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems.Previous approaches have primarily utilized convolutional neural networks(CNNs)to effectively integrate spatial and semantic information.However,the performance of CNN-based methods remains limited in classification accuracy,primarily due to insufficient exploration of local image characteristics.Unlike CNNs,Vision Transformer(ViT)captures discriminative features by modeling relationships between local image patches.However,such methods typically require a large number of training samples to perform effectively.In the context of foreign body classification on coal conveyor belts,the limited availability of training samples hinders the full exploitation of Vision Transformer’s(ViT)capabilities.To address this issue,we propose an efficient approach,termed Key Part-level Attention Vision Transformer(KPA-ViT),which incorporates key local information into the transformer architecture to enrich the training information.It comprises three main components:a key-point detection module,a key local mining module,and an attention module.To extract key local regions,a key-point detection strategy is first employed to identify the positions of key points.Subsequently,the key local mining module extracts the relevant local features based on these detected points.Finally,an attention module composed of self-attention and cross-attention blocks is introduced to integrate global and key part-level information,thereby enhancing the model’s ability to learn discriminative features.Compared to recent transformer-based frameworks—such as ViT,Swin-Transformer,and EfficientViT—the proposed KPA-ViT achieves performance improvements of 9.3%,6.6%,and 2.8%,respectively,on the CUMT-BelT dataset,demonstrating its effectiveness.
基金supported by the National Natural Science Foundation of China(Grant No.62033007)the Major Fundamental Research Program of Shandong Province(Grant No.ZR2023ZD37).
文摘Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limited receptive fields,making it difficult to capture global feature dependencies which is important for object detection,especially when the target undergoes large-scale variations or movement.In view of this,we develop a novel network called effective convolution mixed Transformer Siamese network(SiamCMT)for visual tracking,which integrates CNN-based and Transformer-based architectures to capture both local information and long-range dependencies.Specifically,we design a Transformer-based module named lightweight multi-head attention(LWMHA)which can be flexibly embedded into stage-wise CNNs and improve the network’s representation ability.Additionally,we introduce a stage-wise feature aggregation mechanism which integrates features learned from multiple stages.By leveraging both location and semantic information,this mechanism helps the SiamCMT to better locate and find the target.Moreover,to distinguish the contribution of different channels,a channel-wise attention mechanism is introduced to enhance the important channels and suppress the others.Extensive experiments on seven challenging benchmarks,i.e.,OTB2015,UAV123,GOT10K,LaSOT,DTB70,UAVTrack112_L,and VOT2018,demonstrate the effectiveness of the proposed algorithm.Specially,the proposed method outperforms the baseline by 3.5%and 3.1%in terms of precision and success rates with a real-time speed of 59.77 FPS on UAV123.
基金supported by the National Research Foundation of Korea(NRF)grant funded by theKorea government(MSIT)(No.RS-2024-00405278)partially supported by the Jeju Industry-University Convergence District Project for Promoting Industry-Campus Cooperationfunded by the Ministry of Trade,Industry and Energy(MOTIE,Korea)[Project Name:Jeju Industry-University Convergence District Project for Promoting Industry-Campus Cooperation/Project Number:P0029950].
文摘Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood scenarios involving reflections,occlusions,or indistinct boundaries due to limited contextual modeling.To address these challenges,we propose a hybrid flood segmentation framework that integrates a Vision Transformer(ViT)encoder with a U-Net decoder,enhanced by a novel Flood-Aware Refinement Block(FARB).The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms.We evaluate our model on a UAV-acquired flood imagery dataset,demonstrating that the proposed ViTUNet+FARB architecture outperforms existing CNN and Transformer-based models in terms of accuracy and mean Intersection over Union(mIoU).Detailed ablation studies further validate the contribution of each component,confirming that the FARB design significantly enhances segmentation quality.To its better performance and computational efficiency,the proposed framework is well-suited for flood monitoring and disaster response applications,particularly in resource-constrained environments.
文摘针对当前遥感农作物分类研究中深度学习模型对光谱时间和空间信息特征采样不足,农作物提取仍然存在边界模糊、漏提、误提的问题,提出了一种名为视觉Transformer-长短期记忆递归神经网络(Vision Transformer-long short term memory,ViTL)的深度学习模型,ViTL模型集成了双路Vision-Transformer特征提取、时空特征融合和长短期记忆递归神经网络(LSTM)时序分类等3个关键模块,双路Vision-Transformer特征提取模块用于捕获图像的时空特征相关性,一路提取空间分类特征,一路提取时间变化特征;时空特征融合模块用于将多时特征信息进行交叉融合;LSTM时序分类模块捕捉多时序的依赖关系并进行输出分类。综合利用基于多时序卫星影像的遥感技术理论和方法,对黑龙江省齐齐哈尔市讷河市作物信息进行提取,研究结果表明,ViTL模型表现出色,其总体准确率(Overall Accuracy,OA)、平均交并比(Mean Intersection over Union,MIoU)和F1分数分别达到0.8676、0.6987和0.8175,与其他广泛使用的深度学习方法相比,包括三维卷积神经网络(3-D CNN)、二维卷积神经网络(2-D CNN)和长短期记忆递归神经网络(LSTM),ViTL模型的F1分数提高了9%~12%,显示出显著的优越性。ViTL模型克服了面对多时序遥感影像的农作物分类任务中的时间和空间信息特征采样不足问题,为准确、高效地农作物分类提供了新思路。
基金financially supported by grants from the National Natural Science Foundation of China,No.61170136,61373101,61472270,and 61402318Natural Science Foundation(Youth Science and Technology Research Foundation)of Shanxi Province,No.2014021022-5Shanxi Provincial Key Science and Technology Projects(Agriculture),No.20130311037-4
文摘It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.
基金supported by the National Natural Science Foundation of China under Grant 62177029the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX21_0740),China.
文摘Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.
文摘【目的】高分辨率遥感影像语义分割通过精准提取地物信息,为城市规划、土地分析利用提供了重要的数据支持。当前分割方法通常将遥感影像划分为标准块,进行多尺度局部分割和层次推理,未充分考虑影像中的上下文先验知识和局部特征交互能力,影响了推理分割质量。【方法】为了解决这一问题,本文提出了一种联合跨尺度注意力和语义视觉Transformer的遥感影像分割框架(Cross-scale Attention Transformer,CATrans),融合跨尺度注意力模块和语义视觉Transformer,提取上下文先验知识增强局部特征表示和分割性能。首先,跨尺度注意力模块通过空间和通道两个维度进行并行特征处理,分析浅层-深层和局部-全局特征之间的依赖关系,提升对遥感影像中不同粒度对象的注意力。其次,语义视觉Transformer通过空间注意力机制捕捉上下文语义信息,建模语义信息之间的依赖关系。【结果】本文在DeepGlobe、Inria Aerial和LoveDA数据集上进行对比实验,结果表明:CATrans的分割性能优于现有的WSDNet(Discrete Wavelet Smooth Network)和ISDNet(Integrating Shallow and Deep Network)等分割算法,分别取得了76.2%、79.2%、54.2%的平均交并比(Mean Intersection over Union,mIoU)和86.5%、87.8%、66.8%的平均F1得分(Mean F1 Score,mF1),推理速度分别达到38.1 FPS、13.2 FPS和95.22 FPS。相较于本文所对比的最佳方法WSDNet,mIoU和mF1在3个数据集中分别提升2.1%、4.0%、5.3%和1.3%、1.8%、5.6%,在每类地物的分割中都具有显著优势。【结论】本方法实现了高效率、高精度的高分辨率遥感影像语义分割。
