We know that SME’s that trade online grow faster and create more jobs than those that only operate in their domestic markets.The Internet is breaking down many traditional barriers to global trade,but there is still ...We know that SME’s that trade online grow faster and create more jobs than those that only operate in their domestic markets.The Internet is breaking down many traditional barriers to global trade,but there is still much governments can do to speed and enable SME digitization and ecommerce.The opportunity is huge at展开更多
Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection....Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection.However,the existing networks encounters challenges due to the complex backgrounds,diverse scales and irregular distribution of ship targets.To address these issues,this article proposes a detection algorithm that integrates global context of the images(GCF-Net).First,we construct a global feature extraction module in the backbone network of GCF-Net,which encodes features along different spatial directions.Then,we incorporate bi-directional feature pyramid network(BiFPN)in the neck network to fuse the multi-scale features selectively.Finally,we design a convolution and transformer mixed(CTM)detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images.Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.展开更多
The global context(GC) descriptor is improved for describing interest regions,uses gradient orientation for binning,and thus provides more robust invariance for geometric and photometric transformations.The performanc...The global context(GC) descriptor is improved for describing interest regions,uses gradient orientation for binning,and thus provides more robust invariance for geometric and photometric transformations.The performance of the improved GC(IGC) to image matching is studied through extensive experiments on the Oxford A?ne dataset.Empirical results indicate that the proposed IGC yields quite stable and robust results,signi?cantly outperforms the original GC,and also can outperform the classical scale-invariant feature transform(SIFT) in most of the test cases.By integrating the IGC to the SIFT,the resulting of hybrid SIFT+IGC performs best over all other single descriptors in these experimental evaluations with various geometric transformations.展开更多
A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chin...A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.展开更多
The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of...The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of food for a world of nine billion people in 2050 cannot be taken for grant- ed （Godfray etal. 2010; Swinnen and Squicciarini 2012;展开更多
The international conference on mountain development in a context of global change with special focus on the Himalayas was held in Kathmandu, Nepal on April 21-26.
Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. Thi...Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.展开更多
Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global conte...Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global context for documentlevel neural machine translation(NMT).This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence.With this hierarchical architecture,we feedback the extracted document-level global context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.Notably,we explore the effect of three popular attention functions during the information backward-distribution phase to take a deep look into the global context information distribution of our model.In addition,since large-scale in-domain document-level parallel corpora are usually unavailable,we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability.Experimental results of our model on Chinese-English and English-German corpora significantly improve the Transformer baseline by 4.5 BLEU points on average which demonstrates the effectiveness of our proposed hierarchical model in document-level NMT.展开更多
This paper investigates the role of global context for crowd counting.Specifically,a pure transformer is used to extract features with global information from overlapping image patches.Inspired by classification,we ad...This paper investigates the role of global context for crowd counting.Specifically,a pure transformer is used to extract features with global information from overlapping image patches.Inspired by classification,we add a context token to the input sequence,to facilitate information exchange with tokens corresponding to image patches throughout transformer layers.Due to the fact that transformers do not explicitly model the tried-and-true channel-wise interactions,we propose a token-attention module(TAM)to recalibrate encoded features through channel-wise attention informed by the context token.Beyond that,it is adopted to predict the total person count of the image through regression-token module(RTM).Extensive experiments on various datasets,including ShanghaiTech,UCFQNRF,JHU-CROWD++and NWPU,demonstrate that the proposed context extraction techniques can significantly improve the performanceover the baselines.展开更多
Intercultural trust in global contexts plays a central role in helping people from different cultures to communicate comfortably,which is essential for cooperation.Attempting to construct a framework that might foster...Intercultural trust in global contexts plays a central role in helping people from different cultures to communicate comfortably,which is essential for cooperation.Attempting to construct a framework that might foster international cooperation,and thus be helpful for coping with global emergencies,we relate a Western nomological approach to an Eastern systems approach to analyse intercultural trust in global contexts.Considering cultural impacts on intercultural trust and the nomological framework of cultural differences,we propose an intercultural trust model to interpret how cultural differences influence trust.A qualitative study of Chinese-Irish interactions was conducted to interpret this model.We organized 10 seminars on intercultural trust,and interviewed 16 people to further explore the respondents'deeper feelings and experiences about intercultural trust in global contexts.Through this study,we have identified factors impacting on intercultural trust,and found that intercultural trust can be developed and improved in various ways.To llustrate these ways,we have provided tactics and methods for building intercultural trust in global contexts.Implications are highlighted for organizations to avoid cultural clashes and relevant political or economic risks.展开更多
Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, ...Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the alphamatting.com benchmark quantitatively and qualitatively.展开更多
The publication of Tokyo Trial:Evidence and Judgment of the Nanjing Massacre by the Foreign Languages Press in September is very timely.It has particular relevance in today's global context,marked by increasing ar...The publication of Tokyo Trial:Evidence and Judgment of the Nanjing Massacre by the Foreign Languages Press in September is very timely.It has particular relevance in today's global context,marked by increasing armed conflicts,growing tensions between countries and numerous situations of ethnic and religious persecution.The Nanjing Massacre of winter 1937-38 committed by the Japanese invaders included the killing of more than 300,000 Chinese civilians and unarmed soldiers,the rape of tens of thousands of women,the torture of detainees and the looting and plundering of the population's material goods.展开更多
The 25th Meeting of the Council of Heads of State of the Shanghai Cooperation Organization(SCO)took place in Tianjin from August 31 to September 1.Within the complex global context,this gathering was a strategic con-v...The 25th Meeting of the Council of Heads of State of the Shanghai Cooperation Organization(SCO)took place in Tianjin from August 31 to September 1.Within the complex global context,this gathering was a strategic con-vergence of SCO members,reflecting their joint commitment to navigating a changing era.展开更多
We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To some extent,our method solves the two difficulties encountered in traditional video o...We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past frames.The algorithm uses a global context(GC)module to achieve highperformance,real-time segmentation.The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time.Moreover,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current frame.The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources.We added a refinement module to the decoder to improve boundary segmentation.Our model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.展开更多
Objective Vertebral segmentation in computed tomography(CT)images remains an essential issue in medical image analysis,stemming from the variability in vertebral shapes,high complex deformations,and the inherent ambig...Objective Vertebral segmentation in computed tomography(CT)images remains an essential issue in medical image analysis,stemming from the variability in vertebral shapes,high complex deformations,and the inherent ambiguity in CT scans.The purpose of this study was to develop advanced methods to effectively address this challenging task.Methods We proposed an attention-driven asymmetric convolution deep learning(AACDL)framework for verte-bral segmentation.Specifically,our approach involved constructing a novel asymmetric convolutional U-shaped deep learning architecture to enhance the feature extraction capabilities by increasing its depth for capturing richer spatial details.Further,we constructed a pyramid global context module that integrates global context information through pyramid pooling to boost segmentation accuracy particularly in smaller anatomical regions.Sequential channel and spatial attention mechanisms were also implemented within the network to enable it to automatically concentrate on learning the most salient features and regions across different dimensions.Results The performance precision of our network was rigorously assessed using a suite of four benchmark metrics:the dice coefficient,mean intersection over union(mIoU),precision rate,and F1-score.When compared against the ground truth,our model delivered outstanding scores,attaining a dice coefficient of 82.79%,an mIoU of 90.72%,a precision rate of 90.19%,and an F1-score of 90.09%,each reflecting the commendable accuracy and reliability of our network’s segmentation output.Conclusion The proposed AACDL method might successfully realize accurate segmentation of vertebral CT images,thereby demonstrating significant potential for clinical applications with its robust performance metrics.Its ability to handle the complexities associated with vertebral segmentation may pave the way for enhanced diagnostic and treatment planning processes in healthcare settings.展开更多
文摘We know that SME’s that trade online grow faster and create more jobs than those that only operate in their domestic markets.The Internet is breaking down many traditional barriers to global trade,but there is still much governments can do to speed and enable SME digitization and ecommerce.The opportunity is huge at
基金supported by the National Science Fund for Distinguished Young Scholars(No.62325104).
文摘Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection.However,the existing networks encounters challenges due to the complex backgrounds,diverse scales and irregular distribution of ship targets.To address these issues,this article proposes a detection algorithm that integrates global context of the images(GCF-Net).First,we construct a global feature extraction module in the backbone network of GCF-Net,which encodes features along different spatial directions.Then,we incorporate bi-directional feature pyramid network(BiFPN)in the neck network to fuse the multi-scale features selectively.Finally,we design a convolution and transformer mixed(CTM)detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images.Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.
基金the National Natural Science Foundation of China(Nos.60970109 and 61170228)
文摘The global context(GC) descriptor is improved for describing interest regions,uses gradient orientation for binning,and thus provides more robust invariance for geometric and photometric transformations.The performance of the improved GC(IGC) to image matching is studied through extensive experiments on the Oxford A?ne dataset.Empirical results indicate that the proposed IGC yields quite stable and robust results,signi?cantly outperforms the original GC,and also can outperform the classical scale-invariant feature transform(SIFT) in most of the test cases.By integrating the IGC to the SIFT,the resulting of hybrid SIFT+IGC performs best over all other single descriptors in these experimental evaluations with various geometric transformations.
基金Supported by the National Natural Science Foundation of China(No.61303179,U1135005,61175020)
文摘A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.
基金financial support from the National Nonprofit Institute Research Grant of Chinese Academy of Agricultural Sciences (CAAS, IARRP-2015-28)the logistical support from the CAAS-UGent Joint Labooratory of Global Change and Food Security
文摘The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of food for a world of nine billion people in 2050 cannot be taken for grant- ed （Godfray etal. 2010; Swinnen and Squicciarini 2012;
文摘The international conference on mountain development in a context of global change with special focus on the Himalayas was held in Kathmandu, Nepal on April 21-26.
基金supported by National Natural Science Foundation of China(No.61973009).
文摘Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China under Grant Nos.61751206,61673290 and 61876118the Postgraduate Research&Practice Innovation Program of Jiangsu Province of China under Grant No.KYCX20_2669a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global context for documentlevel neural machine translation(NMT).This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence.With this hierarchical architecture,we feedback the extracted document-level global context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.Notably,we explore the effect of three popular attention functions during the information backward-distribution phase to take a deep look into the global context information distribution of our model.In addition,since large-scale in-domain document-level parallel corpora are usually unavailable,we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability.Experimental results of our model on Chinese-English and English-German corpora significantly improve the Transformer baseline by 4.5 BLEU points on average which demonstrates the effectiveness of our proposed hierarchical model in document-level NMT.
文摘This paper investigates the role of global context for crowd counting.Specifically,a pure transformer is used to extract features with global information from overlapping image patches.Inspired by classification,we add a context token to the input sequence,to facilitate information exchange with tokens corresponding to image patches throughout transformer layers.Due to the fact that transformers do not explicitly model the tried-and-true channel-wise interactions,we propose a token-attention module(TAM)to recalibrate encoded features through channel-wise attention informed by the context token.Beyond that,it is adopted to predict the total person count of the image through regression-token module(RTM).Extensive experiments on various datasets,including ShanghaiTech,UCFQNRF,JHU-CROWD++and NWPU,demonstrate that the proposed context extraction techniques can significantly improve the performanceover the baselines.
基金supported by the National Natural Science Foundation of China under Grant No.72171187the International Cooperation Project of Shaanxi Science and Technology under Grant No.2022WGZJ-15.
文摘Intercultural trust in global contexts plays a central role in helping people from different cultures to communicate comfortably,which is essential for cooperation.Attempting to construct a framework that might foster international cooperation,and thus be helpful for coping with global emergencies,we relate a Western nomological approach to an Eastern systems approach to analyse intercultural trust in global contexts.Considering cultural impacts on intercultural trust and the nomological framework of cultural differences,we propose an intercultural trust model to interpret how cultural differences influence trust.A qualitative study of Chinese-Irish interactions was conducted to interpret this model.We organized 10 seminars on intercultural trust,and interviewed 16 people to further explore the respondents'deeper feelings and experiences about intercultural trust in global contexts.Through this study,we have identified factors impacting on intercultural trust,and found that intercultural trust can be developed and improved in various ways.To llustrate these ways,we have provided tactics and methods for building intercultural trust in global contexts.Implications are highlighted for organizations to avoid cultural clashes and relevant political or economic risks.
基金supported by the National Natural Science Foundation of China under Grant No.62076162the Shanghai Municipal Science and Technology Major Project under Grant Nos.2021SHZDZX0102 and 20511100300.
文摘Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the alphamatting.com benchmark quantitatively and qualitatively.
文摘The publication of Tokyo Trial:Evidence and Judgment of the Nanjing Massacre by the Foreign Languages Press in September is very timely.It has particular relevance in today's global context,marked by increasing armed conflicts,growing tensions between countries and numerous situations of ethnic and religious persecution.The Nanjing Massacre of winter 1937-38 committed by the Japanese invaders included the killing of more than 300,000 Chinese civilians and unarmed soldiers,the rape of tens of thousands of women,the torture of detainees and the looting and plundering of the population's material goods.
文摘The 25th Meeting of the Council of Heads of State of the Shanghai Cooperation Organization(SCO)took place in Tianjin from August 31 to September 1.Within the complex global context,this gathering was a strategic con-vergence of SCO members,reflecting their joint commitment to navigating a changing era.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.61802197,62072449,and 61632003)the Science and Technology Development Fund,Macao SAR(Grant Nos.0018/2019/AKP and SKL-IOTSC(UM)-2021-2023)+1 种基金the Guangdong Science and Technology Department(Grant No.2020B1515130001)University of Macao(Grant Nos.MYRG2020-00253-FST and MYRG2022-00059-FST).
文摘We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past frames.The algorithm uses a global context(GC)module to achieve highperformance,real-time segmentation.The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time.Moreover,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current frame.The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources.We added a refinement module to the decoder to improve boundary segmentation.Our model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.
基金supported by the Special Project of Doctoral Research Innovation Team of Guangdong Polytechnic of Science and Technology(Grant No.XJBS202301).
文摘Objective Vertebral segmentation in computed tomography(CT)images remains an essential issue in medical image analysis,stemming from the variability in vertebral shapes,high complex deformations,and the inherent ambiguity in CT scans.The purpose of this study was to develop advanced methods to effectively address this challenging task.Methods We proposed an attention-driven asymmetric convolution deep learning(AACDL)framework for verte-bral segmentation.Specifically,our approach involved constructing a novel asymmetric convolutional U-shaped deep learning architecture to enhance the feature extraction capabilities by increasing its depth for capturing richer spatial details.Further,we constructed a pyramid global context module that integrates global context information through pyramid pooling to boost segmentation accuracy particularly in smaller anatomical regions.Sequential channel and spatial attention mechanisms were also implemented within the network to enable it to automatically concentrate on learning the most salient features and regions across different dimensions.Results The performance precision of our network was rigorously assessed using a suite of four benchmark metrics:the dice coefficient,mean intersection over union(mIoU),precision rate,and F1-score.When compared against the ground truth,our model delivered outstanding scores,attaining a dice coefficient of 82.79%,an mIoU of 90.72%,a precision rate of 90.19%,and an F1-score of 90.09%,each reflecting the commendable accuracy and reliability of our network’s segmentation output.Conclusion The proposed AACDL method might successfully realize accurate segmentation of vertebral CT images,thereby demonstrating significant potential for clinical applications with its robust performance metrics.Its ability to handle the complexities associated with vertebral segmentation may pave the way for enhanced diagnostic and treatment planning processes in healthcare settings.
