Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accur...Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.展开更多
To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming c...To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.展开更多
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...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.展开更多
Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly...Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.展开更多
Low-resource text plagiarism detection faces a significant challenge due to the limited availability of labeled data for training.This task requires the development of sophisticated algorithms capable of identifying s...Low-resource text plagiarism detection faces a significant challenge due to the limited availability of labeled data for training.This task requires the development of sophisticated algorithms capable of identifying similarities and differences in texts,particularly in the realm of semantic rewriting and translation-based plagiarism detection.In this paper,we present an enhanced attentive Siamese Long Short-Term Memory(LSTM)network designed for Tibetan-Chinese plagiarism detection.Our approach begins with the introduction of translation-based data augmentation,aimed at expanding the bilingual training dataset.Subsequently,we propose a pre-detection method leveraging abstract document vectors to enhance detection efficiency.Finally,we introduce an improved attentive Siamese LSTM network tailored for Tibetan-Chinese plagiarism detection.We conduct comprehensive experiments to showcase the effectiveness of our proposed plagiarism detection framework.展开更多
红外与可见光图像融合方法中存在信息提取和特征解耦不充分、可解释性较低等问题,为了充分提取并融合源图像有效信息,本文提出了一种基于信息瓶颈孪生自编码网络的红外与可见光图像融合方法(DIBF:Double Information Bottleneck Fusion...红外与可见光图像融合方法中存在信息提取和特征解耦不充分、可解释性较低等问题,为了充分提取并融合源图像有效信息,本文提出了一种基于信息瓶颈孪生自编码网络的红外与可见光图像融合方法(DIBF:Double Information Bottleneck Fusion)。该方法通过在孪生分支上构建信息瓶颈模块实现互补特征与冗余特征的解耦,进而将互补信息的表达过程对应于信息瓶颈前半部分的特征拟合过程,将冗余特征的压缩过程对应于信息瓶颈后半部分的特征压缩过程,巧妙地将图像融合中信息提取与融合表述为信息瓶颈权衡问题,通过寻找信息最优表达来实现融合。在信息瓶颈模块中,网络通过训练得到特征的信息权重图,并依据信息权重图,使用均值特征对冗余特征进行压缩,同时通过损失函数促进互补信息的表达,压缩与表达两部分权衡优化同步进行,冗余信息和互补信息也在此过程中得到解耦。在融合阶段,将信息权重图应用在融合规则中,提高了融合图像的信息丰富性。通过在标准图像TNO数据集上进行主客观实验,与传统和近来融合方法进行比较分析,结果显示本文方法能有效融合红外与可见光图像中的有用信息,在视觉感知和定量指标上均取得较好的效果。展开更多
This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles(UAVs).Most Siamese trackers are used to regard object tracking as classification and regression problems.However,it is difficult ...This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles(UAVs).Most Siamese trackers are used to regard object tracking as classification and regression problems.However,it is difficult for these trackers to accurately classify in the face of similar objects,background clutters and other common challenges in UAV scenes.So,a reliable classifier is the key to improving UAV tracking performance.In this paper,a simple yet efficient tracker following the basic architecture of the Siamese neural network is proposed,which improves the classification ability from three stages.First,the frequency channel attention module is introduced to enhance the target features via frequency domain learning.Second,a template-guided attention module is designed to promote information exchange between the template branch and the search branch,which can get reliable classification response maps.Third,adaptive cross-entropy loss is proposed to make the tracker focus on hard samples that contribute more to the training process,solving the data imbalance between positive and negative samples.To evaluate the performance of the proposed tracker,comprehensive experiments are conducted on two challenging aerial datasets,including UAV123 and UAVDT.Experimental results demonstrate that the proposed tracker achieves favorable tracking performances in aerial benchmarks beyond 41 frames/s.We conducted experiments in real UAV scenes to further verify the efficiency of our tracker in the real world.展开更多
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.Howev...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.展开更多
Arabidopsis trichomes are large branched single cells that protrude from the epidermis. The first mor- phological indication of trichome development is an increase in nuclear content resulting from an initial cycle of...Arabidopsis trichomes are large branched single cells that protrude from the epidermis. The first mor- phological indication of trichome development is an increase in nuclear content resulting from an initial cycle of endoreduplication. Our previous study has shown that the C2H2 zinc finger protein GLABROUS INFLORESCENCE STEMS (GIS) is required for trichome initiation in the inflorescence organ and for trichome branching in response to gibberellic acid signaling, although GIS gene does not play a direct role in regulating trichome cell division. Here, we describe a novel role of GIS, controlling trichome cell division indirectly by interacting genetically with a key endoreduplication regulator SIAMESE (SIM). Our molecular and genetic studies have shown that GIS might indireclty control cell division and trichome branching by acting downstream of SIM. A loss of function mutation of SIM signficantly reduced the expression of GIS. Futhermore, the overexpression of GIS rescued the trichome cluster cell phenotypes of sim mutant. The gain or loss of function of GIS had no significant effect on the expression of SIM. These results suggest that GIS may play an indirect role in regulating trichome cell division by genetically interacting with SIM.展开更多
As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts h...As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.展开更多
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 prior...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.展开更多
Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measureme...Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images.An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data.The experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing works.This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.展开更多
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 compl...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.展开更多
文摘Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.
基金supported by the National Natural Science Foundation of China(U19B2016)Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University。
文摘To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.
文摘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.
基金This work is supported by the Natural Science Foundation of China(No.82372035)National Transportation Preparedness Projects(No.ZYZZYJ).Light of West China(No.XAB2022YN10)The China Postdoctoral Science Foundation(No.2023M740760).
文摘Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.
基金supported by the National Natural Science Foundation of China(No.62271456)the Open Projects Program of State Key Laboratory of Multimodal Artificial Intelligence Systems.
文摘Low-resource text plagiarism detection faces a significant challenge due to the limited availability of labeled data for training.This task requires the development of sophisticated algorithms capable of identifying similarities and differences in texts,particularly in the realm of semantic rewriting and translation-based plagiarism detection.In this paper,we present an enhanced attentive Siamese Long Short-Term Memory(LSTM)network designed for Tibetan-Chinese plagiarism detection.Our approach begins with the introduction of translation-based data augmentation,aimed at expanding the bilingual training dataset.Subsequently,we propose a pre-detection method leveraging abstract document vectors to enhance detection efficiency.Finally,we introduce an improved attentive Siamese LSTM network tailored for Tibetan-Chinese plagiarism detection.We conduct comprehensive experiments to showcase the effectiveness of our proposed plagiarism detection framework.
文摘红外与可见光图像融合方法中存在信息提取和特征解耦不充分、可解释性较低等问题,为了充分提取并融合源图像有效信息,本文提出了一种基于信息瓶颈孪生自编码网络的红外与可见光图像融合方法(DIBF:Double Information Bottleneck Fusion)。该方法通过在孪生分支上构建信息瓶颈模块实现互补特征与冗余特征的解耦,进而将互补信息的表达过程对应于信息瓶颈前半部分的特征拟合过程,将冗余特征的压缩过程对应于信息瓶颈后半部分的特征压缩过程,巧妙地将图像融合中信息提取与融合表述为信息瓶颈权衡问题,通过寻找信息最优表达来实现融合。在信息瓶颈模块中,网络通过训练得到特征的信息权重图,并依据信息权重图,使用均值特征对冗余特征进行压缩,同时通过损失函数促进互补信息的表达,压缩与表达两部分权衡优化同步进行,冗余信息和互补信息也在此过程中得到解耦。在融合阶段,将信息权重图应用在融合规则中,提高了融合图像的信息丰富性。通过在标准图像TNO数据集上进行主客观实验,与传统和近来融合方法进行比较分析,结果显示本文方法能有效融合红外与可见光图像中的有用信息,在视觉感知和定量指标上均取得较好的效果。
基金This study was co-supported by the National Natural Science Foundation of China(Nos.61673017 and 61403398).
文摘This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles(UAVs).Most Siamese trackers are used to regard object tracking as classification and regression problems.However,it is difficult for these trackers to accurately classify in the face of similar objects,background clutters and other common challenges in UAV scenes.So,a reliable classifier is the key to improving UAV tracking performance.In this paper,a simple yet efficient tracker following the basic architecture of the Siamese neural network is proposed,which improves the classification ability from three stages.First,the frequency channel attention module is introduced to enhance the target features via frequency domain learning.Second,a template-guided attention module is designed to promote information exchange between the template branch and the search branch,which can get reliable classification response maps.Third,adaptive cross-entropy loss is proposed to make the tracker focus on hard samples that contribute more to the training process,solving the data imbalance between positive and negative samples.To evaluate the performance of the proposed tracker,comprehensive experiments are conducted on two challenging aerial datasets,including UAV123 and UAVDT.Experimental results demonstrate that the proposed tracker achieves favorable tracking performances in aerial benchmarks beyond 41 frames/s.We conducted experiments in real UAV scenes to further verify the efficiency of our tracker in the real world.
基金funded by the National Natural Science Foundation of China(Grant No.52072408),author Y.C.
文摘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.
基金supported by the National Natural Science Foundation of China (Nos. 30970167 and 31228002)the Zhejiang Qianjiang Talent Program (No. 2010R10084)+1 种基金the Zhejiang Provincial Natural Science Foundation of China (No. Z31100041)the Zhejiang Province Foundation for Returned Scholars (No. 20100129), China
文摘Arabidopsis trichomes are large branched single cells that protrude from the epidermis. The first mor- phological indication of trichome development is an increase in nuclear content resulting from an initial cycle of endoreduplication. Our previous study has shown that the C2H2 zinc finger protein GLABROUS INFLORESCENCE STEMS (GIS) is required for trichome initiation in the inflorescence organ and for trichome branching in response to gibberellic acid signaling, although GIS gene does not play a direct role in regulating trichome cell division. Here, we describe a novel role of GIS, controlling trichome cell division indirectly by interacting genetically with a key endoreduplication regulator SIAMESE (SIM). Our molecular and genetic studies have shown that GIS might indireclty control cell division and trichome branching by acting downstream of SIM. A loss of function mutation of SIM signficantly reduced the expression of GIS. Futhermore, the overexpression of GIS rescued the trichome cluster cell phenotypes of sim mutant. The gain or loss of function of GIS had no significant effect on the expression of SIM. These results suggest that GIS may play an indirect role in regulating trichome cell division by genetically interacting with SIM.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea,funded by the Ministry of Education(Nos.NRF2017R1A6A1A03015496,RS-2023-00249555).
文摘As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.
基金support of the National Natural Science Foundation of China (Grant No.52127809,author Z.W,http://www.nsfc.gov.cn/No.51625501,author Z.W,http://www.nsfc.gov.cn/)is greatly appreciated.
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.32072788,31902210)the National Key Research and Development Program of China(Grant No.2019YFE0125600)the Postdoctoral Research Start-up Fund of Heilongjiang Province(Grant No.LBH-Q21062)and the Earmarked Fund for CARS36.
文摘Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images.An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data.The experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing works.This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘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.
