针对复杂背景中行人小目标的检测精度低以及检测不及时的问题,提出了一种改进的Mamba行人小目标检测方法。首先,在主干网络中将标准卷积替换成感受野注意力卷积(RFAConv),通过动态感受野调整了模型对多尺度特征的捕捉能力,同时优化了计...针对复杂背景中行人小目标的检测精度低以及检测不及时的问题,提出了一种改进的Mamba行人小目标检测方法。首先,在主干网络中将标准卷积替换成感受野注意力卷积(RFAConv),通过动态感受野调整了模型对多尺度特征的捕捉能力,同时优化了计算效率。其次,将注意力机制融入视觉状态空间模型(Visual State Space Model,VSSM)中,实现行人小目标多尺度特征的提取。最后,在颈部利用特征增强模块(Feature Enhancement Module,FEM)和双向金字塔模型实现多尺度特征融合。实验结果表明:在HIT-UAV数据集上,改进的Mamba模型实现了81.25%的准确率(以mAP@0.5为标准),比现有的大型模型如YOLOv5、YOLOv8、YOLOv11高出15%以上。展开更多
Brain tumors,one of the most lethal diseases with low survival rates,require early detection and accurate diagnosis to enable effective treatment planning.While deep learning architectures,particularly Convolutional N...Brain tumors,one of the most lethal diseases with low survival rates,require early detection and accurate diagnosis to enable effective treatment planning.While deep learning architectures,particularly Convolutional Neural Networks(CNNs),have shown significant performance improvements over traditional methods,they struggle to capture the subtle pathological variations between different brain tumor types.Recent attention-based models have attempted to address this by focusing on global features,but they come with high computational costs.To address these challenges,this paper introduces a novel parallel architecture,ParMamba,which uniquely integrates Convolutional Attention Patch Embedding(CAPE)and the Conv Mamba block including CNN,Mamba and the channel enhancement module,marking a significant advancement in the field.The unique design of ConvMamba block enhances the ability of model to capture both local features and long-range dependencies,improving the detection of subtle differences between tumor types.The channel enhancement module refines feature interactions across channels.Additionally,CAPE is employed as a downsampling layer that extracts both local and global features,further improving classification accuracy.Experimental results on two publicly available brain tumor datasets demonstrate that ParMamba achieves classification accuracies of 99.62%and 99.35%,outperforming existing methods.Notably,ParMamba surpasses vision transformers(ViT)by 1.37%in accuracy,with a throughput improvement of over 30%.These results demonstrate that ParMamba delivers superior performance while operating faster than traditional attention-based methods.展开更多
针对基于自注意力机制的变换器(Transformer)模型在序列推荐任务中,存在动态用户兴趣捕捉不足以及计算复杂度随序列长度呈平方级增长的问题,提出了基于曼巴2(Mamba2)与自适应时频分析的序列推荐模型(sequential recommendation model ba...针对基于自注意力机制的变换器(Transformer)模型在序列推荐任务中,存在动态用户兴趣捕捉不足以及计算复杂度随序列长度呈平方级增长的问题,提出了基于曼巴2(Mamba2)与自适应时频分析的序列推荐模型(sequential recommendation model based on Mamba2 and adaptive time-frequency analysis,M2ATFSRec),用于提升模型对动态用户兴趣的捕捉能力,进而在降低模型计算复杂度的同时提升模型的推荐精度。首先,采用自适应时频分析方法提取用户历史行为序列的时频特征,对兴趣的多尺度周期模式进行显示编码;然后,利用Mamba2的选择性状态空间机制,实现长序列的高效动态兴趣建模。M2ATFSRec在电影镜头100万条评分(movielens 1 million ratings,MovieLens-1M)、亚马逊美妆产品(Amazon beauty products,Amazon-Beauty)和亚马逊视频游戏(Amazon video games,Amazon-Video-Games)数据集上进行实验,在归一化累积折损增益(normalized discounted cumulative gain,NDCG)指标上相比面向高效的顺序推荐与选择性状态空间模型(towards efficient sequential recommendation with selective state space model,Mamba4Rec)分别提升了6.42%、22.76%和33.22%。该模型在长序列场景下具有更优的推荐性能。展开更多
文摘针对复杂背景中行人小目标的检测精度低以及检测不及时的问题,提出了一种改进的Mamba行人小目标检测方法。首先,在主干网络中将标准卷积替换成感受野注意力卷积(RFAConv),通过动态感受野调整了模型对多尺度特征的捕捉能力,同时优化了计算效率。其次,将注意力机制融入视觉状态空间模型(Visual State Space Model,VSSM)中,实现行人小目标多尺度特征的提取。最后,在颈部利用特征增强模块(Feature Enhancement Module,FEM)和双向金字塔模型实现多尺度特征融合。实验结果表明:在HIT-UAV数据集上,改进的Mamba模型实现了81.25%的准确率(以mAP@0.5为标准),比现有的大型模型如YOLOv5、YOLOv8、YOLOv11高出15%以上。
基金supported by the Outstanding Youth Science and Technology Innovation Team Project of Colleges and Universities in Hubei Province(Grant no.T201923)Key Science and Technology Project of Jingmen(Grant nos.2021ZDYF024,2022ZDYF019)Cultivation Project of Jingchu University of Technology(Grant no.PY201904).
文摘Brain tumors,one of the most lethal diseases with low survival rates,require early detection and accurate diagnosis to enable effective treatment planning.While deep learning architectures,particularly Convolutional Neural Networks(CNNs),have shown significant performance improvements over traditional methods,they struggle to capture the subtle pathological variations between different brain tumor types.Recent attention-based models have attempted to address this by focusing on global features,but they come with high computational costs.To address these challenges,this paper introduces a novel parallel architecture,ParMamba,which uniquely integrates Convolutional Attention Patch Embedding(CAPE)and the Conv Mamba block including CNN,Mamba and the channel enhancement module,marking a significant advancement in the field.The unique design of ConvMamba block enhances the ability of model to capture both local features and long-range dependencies,improving the detection of subtle differences between tumor types.The channel enhancement module refines feature interactions across channels.Additionally,CAPE is employed as a downsampling layer that extracts both local and global features,further improving classification accuracy.Experimental results on two publicly available brain tumor datasets demonstrate that ParMamba achieves classification accuracies of 99.62%and 99.35%,outperforming existing methods.Notably,ParMamba surpasses vision transformers(ViT)by 1.37%in accuracy,with a throughput improvement of over 30%.These results demonstrate that ParMamba delivers superior performance while operating faster than traditional attention-based methods.
文摘针对基于自注意力机制的变换器(Transformer)模型在序列推荐任务中,存在动态用户兴趣捕捉不足以及计算复杂度随序列长度呈平方级增长的问题,提出了基于曼巴2(Mamba2)与自适应时频分析的序列推荐模型(sequential recommendation model based on Mamba2 and adaptive time-frequency analysis,M2ATFSRec),用于提升模型对动态用户兴趣的捕捉能力,进而在降低模型计算复杂度的同时提升模型的推荐精度。首先,采用自适应时频分析方法提取用户历史行为序列的时频特征,对兴趣的多尺度周期模式进行显示编码;然后,利用Mamba2的选择性状态空间机制,实现长序列的高效动态兴趣建模。M2ATFSRec在电影镜头100万条评分(movielens 1 million ratings,MovieLens-1M)、亚马逊美妆产品(Amazon beauty products,Amazon-Beauty)和亚马逊视频游戏(Amazon video games,Amazon-Video-Games)数据集上进行实验,在归一化累积折损增益(normalized discounted cumulative gain,NDCG)指标上相比面向高效的顺序推荐与选择性状态空间模型(towards efficient sequential recommendation with selective state space model,Mamba4Rec)分别提升了6.42%、22.76%和33.22%。该模型在长序列场景下具有更优的推荐性能。
