基于二阶注意力的Siamese网络视觉跟踪算法

为提升基于Siamese网络视觉跟踪算法的特征表达能力和判别能力,以获得更好的跟踪性能,提出了一种轻量级的基于二阶注意力的Siamese网络视觉跟踪算法。使用轻量级VGG-Net作为Siamese网络的主干,获取目标的深度特征;在Siamese网络的末端并行使用所提残差二阶池化网络和二阶空间注意力网络,获取具有通道相关性的二阶注意力特征和具有空间相关性的二阶注意力特征;使用残差二阶通道注意力特征和二阶空间注意力特征,通过双分支响应策略实现视觉跟踪。利用GOT-10k数据集对所提算法进行端到端的训练,并在OTB100和VOT2018数据集上进行验证。实验结果表明:所提算法的跟踪性能取得了显著提升,与基准算法SiamFC相比,在OTB100数据集上,精度和成功率分别提高了0.100和0.096,在VOT2018数据集上,预期平均重叠率(EAO)提高了0.077,跟踪速度达到了48帧/s。

基于区域细化的Siamese网络目标跟踪算法

针对快速运动导致跟踪目标尺度变化大、分辨率低等问题,提出了一种基于区域细化的Siamese网络目标跟踪算法。在Siamese网络中引入多尺度特征感知模型,有效提取深层全局通道特征和局部空间特征,准确提取判别性信息;为进一步在搜索区域增强前景,构建区域细化模型,利用经主干网络提取的目标区域特征对搜索区域目标进行甄别,实现由粗到细的跟踪策略,有效增强目标表征能力。将所提算法在OTB100数据集上与现有的一些跟踪算法进行测试。实验结果表明,本文算法在跟踪成功率与跟踪精度方面均取得了良好的表现。同时在低分辨率、形变、光照变化等方面表现出较强的鲁棒性。

长时视觉跟踪中基于双模板Siamese结构的目标漂移判定网络

在长时视觉跟踪中,大部分目标丢失判定方法需要人为确定阈值,而最优阈值的选取通常较为困难,造成长时跟踪算法的泛化能力较弱。为此,该文提出一种无需人为选取阈值的目标漂移判定网络(DNet)。该网络采用Siamese结构,利用静态模板和动态模板共同判定跟踪结果是否丢失,其中,引入动态模板有效提高算法对目标外观变化的适应能力。为了对所提目标漂移判定网络进行训练,建立了样本丰富的数据集。为验证所提网络的有效性,将该网络与基础跟踪器和重检测模块相结合,构建了一个完整的长时跟踪算法。在UAV20L, LaSOT,VOT2018-LT和VOT2020-LT等经典的视觉跟踪数据集上进行了测试,实验结果表明,相比于基础跟踪器,在UAV20L数据集上,跟踪精度和成功率分别提升了10.4%和7.5%。

基于补偿注意力机制的Siamese网络跟踪算法

为了应对视觉目标跟踪中常见的目标尺寸变化、运动模糊、目标被遮挡、目标受相似物干扰等问题,提出一种基于补偿注意力机制的Siamese网络跟踪算法CDAM-Siam。首先采用Res Net-50网络构建Siamese的骨干网络以进行不同层次的特征提取,加深网络同时充分利用不同层所提取的特征;其次在骨干网络中融入具有补偿机制的双重注意力网络CDAM,强化特征图中的有效特征并减弱一些边缘特征,以提高CDAM-Siam算法面对复杂场景时的鲁棒性;最后构建特征融合网络并将其添加到主干网络中,对来自不同层次的特征图进行有效融合以获得高分辨率和信息丰富的特征图,最终实现准确的目标跟踪。在GOT-10K和You Tube-BB数据集上对CDAM-Siam算法进行训练后,在OTB100数据集上进行检测,结果表明,CDAM-Siam的跟踪成功率和精度分别达到68.3%和89.5%,在面临跟踪任务中的常见挑战时其仍能保持较好的跟踪效果,跟踪速度可达56帧/s,满足实时跟踪需求;在VOT2018数据集中的测试结果显示,该算法的准确率、鲁棒性和平均重叠率分别可达53.8%、39.4%和26.5%。

Malware Detection Using Dual Siamese Network Model

This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber security.Traditional signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new malware.Recently,machine learning-based malware detection techniques,such as Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN),have gained attention.While these methods demonstrate high performance by leveraging static and dynamic features,they are limited in detecting new malware or variants because they learn based on the characteristics of existing malware.To overcome these limitations,malware detection techniques employing One-Shot Learning and Few-Shot Learning have been introduced.Based on this,the Siamese Network,which can effectively learn from a small number of samples and perform predictions based on similarity rather than learning the characteristics of the input data,enables the detection of new malware or variants.We propose a dual Siamese network-based detection framework that utilizes byte images converted frommalware binary data to grayscale,and opcode frequency-based images generated after extracting opcodes and converting them into 2-gramfrequencies.The proposed framework integrates two independent Siamese network models,one learning from byte images and the other from opcode frequency-based images.The detection models trained on the different kinds of images generated separately apply the L1 distancemeasure to the output vectors themodels generate,calculate the similarity,and then apply different weights to each model.Our proposed framework achieved a malware detection accuracy of 95.9%and 99.83%in the experimentsusingdifferentmalware datasets.The experimental resultsdemonstrate that ourmalware detection model can effectively detect malware by utilizing two different types of features and employing the dual Siamese network-based model.

基于多标准和改进Siamese网络的相似航班号判断方法研究

为了合理区分和有效识别不同航班号,首先提出基于多标准的判断准则,使用主成分分析法量化得到统一的相似度;其次建立改进的Siamese网络模型,获得文本的语义信息;最后采用文本之间的Jaro-Winkler距离客观修正网络对比损失函数,综合网络输出判定2个航班号的相似情况。研究结果表明:多标准准则判定方法速度快且通用性强,改进后的Siamese网络虽受到训练样本的直接影响,但收敛速度明显提高,识别率比原网络平均提高约2.7%,比多标准判断准则提高约3%。研究结果可为相似航班号识别与预警提供理论依据。

Siamese Dense Pixel-Level Fusion Network for Real-Time UAV Tracking

Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.

Target tracking method of Siamese networks based on the broad learning system

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

SiamDLA: Dynamic Label Assignment for Siamese Visual Tracking

Label assignment refers to determining positive/negative labels foreach sample to supervise the training process. Existing Siamese-based trackersprimarily use fixed label assignment strategies according to human priorknowledge;thus, they can be sensitive to predefined hyperparameters and failto fit the spatial and scale variations of samples. In this study, we first developa novel dynamic label assignment (DLA) module to handle the diverse datadistributions and adaptively distinguish the foreground from the backgroundbased on the statistical characteristics of the target in visual object tracking.The core of DLA module is a two-step selection mechanism. The first stepselects candidate samples according to the Euclidean distance between trainingsamples and ground truth, and the second step selects positive/negativesamples based on the mean and standard deviation of candidate samples.The proposed approach is general-purpose and can be easily integrated intoanchor-based and anchor-free trackers for optimal sample-label matching.According to extensive experimental findings, Siamese-based trackers withDLA modules can refine target locations and outperformbaseline trackers onOTB100, VOT2019, UAV123 and LaSOT. Particularly, DLA-SiamRPN++improves SiamRPN++ by 1% AUC and DLA-SiamCAR improves Siam-CAR by 2.5% AUC on OTB100. Furthermore, hyper-parameters analysisexperiments show that DLA module hardly increases spatio-temporal complexity,the proposed approach maintains the same speed as the originaltracker without additional overhead.

Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.

SRAFE:Siamese Regression Aesthetic Fusion Evaluation for Chinese Calligraphic Copy

Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and in-heritance.However,previous aesthetic evaluation studies often focussed on photos and paintings,with few attempts on Chinese calligraphy.To solve this problem,a Siamese regression aesthetic fusion method is proposed,named SRAFE,for Chinese calligraphy based on the combination of calligraphy aesthetics and deep learning.First,a dataset termed Evaluated Chinese Calligraphy Copies(E3C)is constructed for aesthetic evalu-ation.Second,12 hand‐crafted aesthetic features based on the shape,structure,and stroke of calligraphy are designed.Then,the Siamese regression network(SRN)is designed to extract the deep aesthetic representation of calligraphy.Finally,the SRAFE method is built by fusing the deep aesthetic features with the hand‐crafted aesthetic features.Experimental results show that scores given by SRAFE are similar to the aesthetic evaluation label of E3C,proving the effectiveness of the authors’method.

基于Siamese LSTM的中文多文档自动文摘模型

在文本信息数量迅速增长的环境下,为提升阅读效率,提出一种基于深度学习的多文档自动文本摘要模型。在传统文摘模型的基础上将Siamese LSTM深度学习网络应用到文本相似度计算中,计算曼哈顿距离来表征文本相似度,并采用去除停用词的方法改进该网络模型以提升计算效率。实验结果表明,使用Siamese LSTM与传统余弦相似度等方法相比,生成的文摘在语义方面更贴近主题,质量更高,整个文摘系统的工作效率也显著提升。

融合LeNet-5和Siamese神经网络模型的人脸认证算法研究

基于人脸信息的身份认证对于个人安全和社会稳定都具有非常重要的意义。传统的人脸认证方法依赖人工构造视觉特征,易受外界条件影响,识别精度不高。深度学习模型以自主学习方式进行特征提取,能从复杂的数据中提取到人脸的隐性特征。然而大部分深度学习人脸认证方法需大量带有身份标记的训练样本,额外增加了标记数据的成本。针对以上问题,提出了融合LeNet-5和Siamese神经网络模型的人脸认证算法。该算法在Siamese神经网络框架基础上,引入LeNet-5卷积神经网络,将单分支LeNet-5卷积网络扩充为结构相同且参数共享的双分支LeNet-5卷积网络,通过缩小卷积核、增加卷积层来调整网络结构,使用Contrastive Loss函数对融合网络进行训练。实验结果表明,该算法在不同的人脸数据集上,均获取较高的识别精度。

改进的Siamese自适应网络和多特征融合跟踪算法

针对当前目标跟踪领域中跟踪精确度和跟踪速度难以平衡的问题,例如基于相关滤波实现的跟踪器能够以很高的速度运行,但跟踪准确性极低;基于深度学习实现的跟踪器能够实现较高的跟踪准确性,但跟踪速度较低。在此基础上,提出一种改进的Siamese自适应网络和多特征融合目标跟踪算法。首先在Siamese网络每个分支上同时构建AlexNet网络和改进的ResNet网络,用于特征提取。其次通过端到端的方式同时进行训练,将跟踪问题分解为分类每个位置标签和回归边界框子问题。最后对浅层特征和深层特征进行自适应选择,并基于多特征融合进行目标识别和定位。将提出的算法与现有的一些跟踪器在目标跟踪标准数据集上进行测试。实验结果表明,提出的算法能够在确保跟踪速度的同时实现较高的跟踪精确度和成功率。同时,在光照变化、形变、背景杂波等复杂情况下,算法具有较强的鲁棒性。

基于双模板Siamese网络的鲁棒视觉跟踪算法

近年来,Siamese网络由于其良好的跟踪精度和较快的跟踪速度,在视觉跟踪领域引起极大关注,但大多数Siamese网络并未考虑模型更新,从而引起跟踪错误。针对这一不足,该文提出一种基于双模板Siamese网络的视觉跟踪算法。首先,保留响应图中响应值稳定的初始帧作为基准模板R,同时使用改进的APCEs模型更新策略确定动态模板T。然后,通过对候选目标区域与2个模板匹配度结果的综合分析,对结果响应图进行融合,以得到更加准确的跟踪结果。在OTB2013和OTB2015数据集上的实验结果表明,与当前5种主流跟踪算法相比,该文算法的跟踪精度和成功率具有明显优势,不仅在尺度变化、平面内旋转、平面外旋转、遮挡、光照变化情况下具有较好的跟踪效果,而且达到了46帧/s的跟踪速度。

基于注意力机制的Siamese目标跟踪算法研究

为了进一步提升Siamese神经网络算法在目标跟踪领域的性能,本文对SiamMask的backbone模型基于注意力机制原理进行了重新设计。首先,对Siamese目标追踪网络的backbone网络框架进行局部修改;其次,对改进的算法与原SiamMask算法在Microsoft COCO2017数据集上进行了网络训练与验证;最后,将原SiamMask算法与改进的算法的验证集数据进行比对,以此来评价改进算法的性能。结果表明,在同等算力与数据集的条件下,基于注意力机制的backbone Siamese目标跟踪算法比SiamMask在IOU值上有2个百分点左右的性能提升。

基于在线学习的Siamese网络视觉跟踪算法

基于Siamese网络的视觉跟踪算法是近年来视觉跟踪领域的一类重要方法,其在跟踪速度和精度上都具有良好的性能。但是大多数基于Siamese网络的跟踪算法依赖离线训练模型,缺乏对跟踪器的在线更新。针对这一问题,本文提出了一种基于在线学习的Siamese网络视觉跟踪算法。该算法采用双模板思想,将第一帧中的目标当作静态模板,在后续帧中使用高置信度更新策略获取动态模板;在线跟踪时,利用快速变换学习模型从双模板中学习目标的表观变化,同时根据当前帧的颜色直方图特征计算出搜索区域的目标似然概率图,与深度特征融合,进行背景抑制学习;最后,将双模板获取的响应图进行加权融合,获得最终跟踪结果。在OTB2015、TempleColor128和VOT数据集上的实验结果表明,本文算法的测试结果与近几年的多种主流算法相比均有所提高,在目标形变、相似背景干扰、快速运动等复杂场景下具有较好的跟踪性能。

基于Siamese卷积神经网络的指静脉识别

针对在指静脉识别中特征提取困难以及深度学习方法在添加新类别时需要重新学习的问题,提出使用Siamese卷积神经网络进行指静脉相似度计算的识别方法。首先利用Sobel算子对图像边缘检测,通过形态学处理提取感兴趣区域(ROI),获取识别图像;其次,构建卷积神经网络提取有效的特征编码,在Siamese网络中使用编码计算距离,并使用三元组损失函数定义目标函数。实验结果表明,在公开的指静脉数据库验证,提出的算法获得较高的识别准确率,在指静脉识别中具有一定的实用价值。

基于Siamese网络的句子相似度计算方法

句子相似度度量是自然语言处理领域的一个重要组成部分。针对传统基于统计学习的方法只能进行表面文本相似度计算,存在着无法提取出文本深层语义信息的问题,提出了一种基于Siamese网络的句子相似度计算方法,利用CNN与BiLSTM相结合的方法来挖掘句子中的深层语义信息,得到句子不同词粒度的向量表示;利用门控机制(transform gate)的思想,对BiLSTM和CNN提取的句子深层语义特征赋予不同的权重,突出对句子贡献度较大的特征,将特征组合成句子的向量表示;最后通过计算出的曼哈顿距离来度量两个句子的相似度。实验结果表明,基于CCKS2018评测项目微众银行客户问句匹配大赛数据集,该算法相比较于其他多种方法准确率和F1值都有不同程度的提高。

基于Siamese卷积神经网络的影像瓦片变化检测技术

针对地理信息变化较快而传统更新方式效率不高的问题,目前许多学者提出了各类变化检测的方法,但这些方法大都是基于影像数据进行试验,对影像预处理要求较高,且检测精度的稳定性较差,受数据源质量影响较大。而天地图、百度地图、谷歌地图等地图中均可免费下载各种级别的影像瓦片,因此本文提出利用天地图影像瓦片进行试验,采用Siamese卷积神经网络(SCNN)和深度学习技术,开发基于SCNN的高精度变化监测算法,以快速发现变化区域,实现地理信息变化信息检测。