In recent years,the concept of digital human has attracted widespread attention from all walks of life,and the modelling of high-fidelity human bodies,heads,and hands has been intensively studied.This paper focuses on...In recent years,the concept of digital human has attracted widespread attention from all walks of life,and the modelling of high-fidelity human bodies,heads,and hands has been intensively studied.This paper focuses on head modelling and proposes a generic head parametric model based on neural radiance fields.Specifically,we first use face recognition networks and 3D facial expression database FaceWarehouse to parameterize identity and expression semantics,respectively,and use both as conditional inputs to build a neural radiance field for the human head,thereby improving the head model’s representation ability while ensuring editing capabilities for the identity and expression of the rendered results;then,through a combination of volume rendering and neural rendering,the 3D representation of the head is rapidly rendered into the 2D plane,producing a high-fidelity image of the human head.Thanks to the well-designed loss functions and good implicit representation of the neural radiance field,our model can not only edit the identity and expression independently,but also freely modify the virtual camera position of the rendering results.It has excellent multi-view consistency,and has many applications in novel view synthesis,pose driving and more.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster...Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.展开更多
Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Conseq...Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.展开更多
全国知识图谱与语义计算大会和知识图谱国际联合会议联办,是由第十八届全国知识图谱与语义计算大会2024(China Conference on Knowledge Graph and Semantic Computing,CCKS 2024)和第十三届知识图谱国际联合会议2024(International Joi...全国知识图谱与语义计算大会和知识图谱国际联合会议联办,是由第十八届全国知识图谱与语义计算大会2024(China Conference on Knowledge Graph and Semantic Computing,CCKS 2024)和第十三届知识图谱国际联合会议2024(International Joint Conference of Knowledge Graph,IJCKG 2024)联合举办。展开更多
全国知识图谱与语义计算大会(China Conference on Knowledge Graph and Semantic Computing,CCKS)由中国中文信息学会语言与知识计算专业委员会主办,大会源自中文知识图谱研讨会(Chinese Knowledge Graph Symposium,CKGS)和中国语义网...全国知识图谱与语义计算大会(China Conference on Knowledge Graph and Semantic Computing,CCKS)由中国中文信息学会语言与知识计算专业委员会主办,大会源自中文知识图谱研讨会(Chinese Knowledge Graph Symposium,CKGS)和中国语义网与万维网科学大会(Chinese Semantic Web and Web Science Conference,CSWS)。展开更多
With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection abil...With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.展开更多
全国知识图谱与语义计算大会(China Conference on Knowledge Graph and Semantic Computing,CCKS)由中国中文信息学会语言与知识计算专业委员会主办,大会源自中文知识图谱研讨会(Chinese Knowledge Graph Symposium,CKGS)和中国语义网...全国知识图谱与语义计算大会(China Conference on Knowledge Graph and Semantic Computing,CCKS)由中国中文信息学会语言与知识计算专业委员会主办,大会源自中文知识图谱研讨会(Chinese Knowledge Graph Symposium,CKGS)和中国语义网与万维网科学大会(Chinese Semantic Web and Web Science Conference,CSW),2016年两会合并,CCKS2016、2017、2018、2019、2020、2021、2022和2023分别在北京、成都、天津、杭州、南昌、广州(线上)、秦皇岛和沈阳举办。展开更多
This paper tries to discuss one realizable mode of SW(Semantic Web). It is called NVS(Network-Virtual Society). SW is regarded as the next-generation Web. By adding semantics into Web,SW provides interoperability betw...This paper tries to discuss one realizable mode of SW(Semantic Web). It is called NVS(Network-Virtual Society). SW is regarded as the next-generation Web. By adding semantics into Web,SW provides interoperability between applications and facilities to enable automated processing of Web resources. Agent will be the executer in the automated process. After analyzing relational theories and technologies, we put forward the concept and mode of NVS,and gives our reason.展开更多
文摘In recent years,the concept of digital human has attracted widespread attention from all walks of life,and the modelling of high-fidelity human bodies,heads,and hands has been intensively studied.This paper focuses on head modelling and proposes a generic head parametric model based on neural radiance fields.Specifically,we first use face recognition networks and 3D facial expression database FaceWarehouse to parameterize identity and expression semantics,respectively,and use both as conditional inputs to build a neural radiance field for the human head,thereby improving the head model’s representation ability while ensuring editing capabilities for the identity and expression of the rendered results;then,through a combination of volume rendering and neural rendering,the 3D representation of the head is rapidly rendered into the 2D plane,producing a high-fidelity image of the human head.Thanks to the well-designed loss functions and good implicit representation of the neural radiance field,our model can not only edit the identity and expression independently,but also freely modify the virtual camera position of the rendering results.It has excellent multi-view consistency,and has many applications in novel view synthesis,pose driving and more.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
基金supported by the National Key Research and Development Program of China(2020YFC1512304).
文摘Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.
基金supported by the National Natural Science Foundation of China(U21B2074,52105070).
文摘Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.
文摘全国知识图谱与语义计算大会和知识图谱国际联合会议联办,是由第十八届全国知识图谱与语义计算大会2024(China Conference on Knowledge Graph and Semantic Computing,CCKS 2024)和第十三届知识图谱国际联合会议2024(International Joint Conference of Knowledge Graph,IJCKG 2024)联合举办。
文摘全国知识图谱与语义计算大会(China Conference on Knowledge Graph and Semantic Computing,CCKS)由中国中文信息学会语言与知识计算专业委员会主办,大会源自中文知识图谱研讨会(Chinese Knowledge Graph Symposium,CKGS)和中国语义网与万维网科学大会(Chinese Semantic Web and Web Science Conference,CSWS)。
基金supported in part by the Tianjin Technology Innovation Guidance Special Fund Project under Grant No.21YDTPJC00850in part by the National Natural Science Foundation of China under Grant No.41906161in part by the Natural Science Foundation of Tianjin under Grant No.21JCQNJC00650。
文摘With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.
文摘全国知识图谱与语义计算大会(China Conference on Knowledge Graph and Semantic Computing,CCKS)由中国中文信息学会语言与知识计算专业委员会主办,大会源自中文知识图谱研讨会(Chinese Knowledge Graph Symposium,CKGS)和中国语义网与万维网科学大会(Chinese Semantic Web and Web Science Conference,CSW),2016年两会合并,CCKS2016、2017、2018、2019、2020、2021、2022和2023分别在北京、成都、天津、杭州、南昌、广州(线上)、秦皇岛和沈阳举办。
文摘This paper tries to discuss one realizable mode of SW(Semantic Web). It is called NVS(Network-Virtual Society). SW is regarded as the next-generation Web. By adding semantics into Web,SW provides interoperability between applications and facilities to enable automated processing of Web resources. Agent will be the executer in the automated process. After analyzing relational theories and technologies, we put forward the concept and mode of NVS,and gives our reason.
