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SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yuan, Zhenlong Yang, Zhidong Cai, Yujun Wu, Kuangxin Liu, Mufan Zhang, Dapeng Jiang, Hao Li, Zhaoxin Wang, Zhaoqi Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China School of Electrical Engineering and Computer Science Australia Hunan Police Academy Information Technology Department Changsha410100 China Shanghai Jiao Tong University Cooperative MediaNet Innovation Center Shanghai200240 China Lanzhou University DSLAB School of Information Science and Engineering 730000 China Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big Data 100081 China

Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas. However, existing approaches neglect to address the problem of deformation instability caused by easily overlooked edge-skipping, potentially leading to matching distortions, thus leaving room for further improvement. To fill this gap, we propose SED-MVS, which adopts panoptic segmentation and multi-trajectory diffusion strategy for segmentation-driven and edge-aligned patch deformation. Specifically, to prevent unanticipated edge-skipping, we first employ SAM2 for panoptic segmentation as depth-edge guidance to guide patch deformation, followed by multi-trajectory diffusion strategy to ensure patches are comprehensively aligned with depth edges. Moreover, to avoid potential inaccuracy of random initialization, we combine both sparse points from LoFTR and monocular depth map from DepthAnything V2 to restore reliable and realistic depth map for initialization and supervised guidance. Finally, we integrate the segmentation image with the monocular depth map to exploit inter-instance occlusion relationship, then further regard them as occlusion map to implement two distinct edge constraint, thereby facilitating occlusion-aware patch deformation. Extensive results on ETH3D, Tanks & Temples, BlendedMVS, Strecha and DL3DV-10K datasets validate the state-of-the-art performance and robust generalization capability of our proposed method. © 1991-2012 IEEE.

关键词： Image segmentation

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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science China(information sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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HIME: Mining and Ensembling Heterogeneous information for Protein-Protein Interactions Prediction

HIME: Mining and Ensembling Heterogeneous Information for Pr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huaming Chen Yaochu Jin Lei Wang Chi-Hung Chi Jun Shen School of Computing and Information Technology University of Wollongong Wollongong NSW Australia Department of Computer Science University of Surrey United Kingdom Data61 CSIRO Australia

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Research on protein-protein interactions (PPIs) data paves the way towards understanding the mechanisms of infectious diseases, however improving the prediction performance of PPIs of inter-species remains a challenge. Since one single type of sequence data such as amino acid composition may be deficient for high-quality prediction of protein interactions, we have investigated a broader range of heterogeneous information of sequences data. This paper proposes a novel framework for PPIs prediction based on Heterogeneous information Mining and Ensembling (HIME) process to effectively learn from the interaction data. In particular, the proposed approach introduces an ensemble process together with substantial features that generate better performance of PPIs prediction task. The performance of the proposed framework is validated on real protein interaction datasets. The extensive experiments show that HIME achieves higher performance over all existing methods reported in literature so far.

关键词： Proteins databases Predictive models data models Pathogens Computational modeling Protein engineering

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Context aware 3d unet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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Machine learning for modelling unstructured grid data in computational physics: a review

arXiv

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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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Deep Learning Based Search and Recommendation of Literature in Wanfang Knowledge Base

Deep Learning Based Search and Recommendation of Literature ...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Tongtong Li Qiang Lin Xiaoyan Chen Yanru Guo Shaofang Zhao School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu China Dynamic Flow Data Computing and Application Laboratory Northwest Minzu University Lanzhou Gansu China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Lanzhou Gansu China

Classifying Chinese text is a hot but difficult topic in natural language processing field. In this work, we aim to quickly recommend the required documents for users via searching keywords in a massive literature database. We use "Wanfang data Knowledge Service Platform Journal Document User Behavior Log data" as the object to research and build a text classifier based on deep learning techniques. It is proposed to perform data cleaning and data extraction for user browsing behavior logs and user download behavior logs. Firstly, Chinese word segmentation and keyword extraction are carried out on the document title information, and the keyword data set and document keyword data set based on the document name are constructed. Finally, using the traditional deep learning model and convolutional neural network model to build a text classifier for training and classification, as a model for users to search for keywords recommended for literature. Experimental results show that the constructed model is able to effectively classify and recommend documents with the users' search keywords, and extract keywords from document names in the construction model. The proposed model TEXTCNN V2 obtains a value for accuracy, precision and F-1 score is 0.9132, 0.9220 and 0.9154, respectively.

关键词： Deep learning Training Computational modeling Text categorization Search problems data models Convolutional neural networks

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Event extraction by associating event types and argument roles

arXiv

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arXiv 2021年

作者： Li, Qian Guo, Shu Wu, Jia Li, Jianxin Sheng, Jiawei Wang, Lihong Dong, Xiaohan Peng, Hao Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computing Macquarie University Sydney Australia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level graph to associate sentence nodes of different types, and adopting a graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a universal schema of argument roles, with a parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & Intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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Controlled Secure Direct Communication Protocol via the Three-Qubit Partially Entangled Set of States

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computers, Materials & Continua 2019年第3期58卷 809-827页

作者： Gang Xu Ke Xiao Zongpeng Li Xin-Xin Niu Michael Ryan School of Information Science and Technology North China University of TechnologyBeijing100144China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science Wuhan UniversityHubei430072China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou UniversityGuiyangGuizhou550025China School of Computing Dublin City UniversityDublin 9Ireland.

In this paper,we first re-examine the previous protocol of controlled quantum secure direct communication of Zhang et al.’s scheme,which was found insecure under two kinds of attacks,fake entangled particles attack and disentanglement ***,by changing the party of the preparation of cluster states and using unitary operations,we present an improved protocol which can avoid these two kinds of ***,the protocol is proposed using the three-qubit partially entangled set of *** is more efficient by only using three particles rather than four or even more to transmit one bit secret *** our using state is much easier to prepare for multiqubit states and our protocol needs less measurement resource,it makes this protocol more convenient from an applied point of view.

关键词： Controlled secure direct communication cluster state three-qubit partially entangled state entanglement swapping.

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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