计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 47-58.doi: 10.11896/j.issn.1002-137X.2019.09.006
周燕, 曾凡智, 吴臣, 罗粤, 刘紫琴
ZHOU Yan, ZENG Fan-zhi, WU Chen, LUO Yue, LIU Zi-qin
摘要: 研究具有低维、高鉴别力的三维形状特征提取方法有助于解决三维形状数据分类和检索等问题。随着深度学习的持续发展,结合深度学习的三维形状特征提取方法已成为研究热点。将深度学习与传统的三维形状特征提取方法相结合,不仅可以突破非深度学习方法的瓶颈,而且可以提高三维形状数据分类、检索等任务的准确率,尤其是当三维形状是非刚体时。然而,深度学习尚在发展中,仍存在需要大量训练样本的问题,因此如何运用深度学习方法来高效提取三维形状特征成为了计算机视觉领域的研究重点和难点。目前,研究者大多从改进网络结构和训练方法等方面入手,着重提高神经网络提取特征的能力。文中结合深度学习和三维形状特征提取方法的发展历程,首先介绍相关深度学习模型,以及网络改进、训练方法等方面的新思路;其次重点对基于深度学习的刚体与非刚体的特征提取方法做综合的阐述,描述当前深度学习方法用于三维形状特征提取的情况;然后简述现有三维形状检索系统的现况以及相似度计算方法;最后介绍当前三维形状特征提取方法存在的问题,并探讨其未来的发展趋势。
中图分类号:
[1]WEI X Z,QIU S Q,ZHU L,et al.Toward Support-Free 3D Printing:A Skeletal Approach for Partitioning Models[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(10):2799-2812. [2]CHU C H,WANG I J,WANG J B,et al.3D parametric human face modeling for personalized product design[J].Advanced Engineering Informatics,2017,32:202-223. [3]FRADI A,LOUHICHI B,MAHJOUB M A,et al.3D ObjectRetrieval Based on Similarity Calculation in 3D Computer Aided Design Systems[C]//Proceedings of 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications.Hammamet:IEEE,2017:160-165. [4]SHAN H,ZHANG Y,YANG Q,et al.3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network[J].IEEE Transactions on Medical Imaging,2018,37(6):1522-1534. [5]HU W F,ZHAO S,REN Y,et al.3D model dynamic cutting technology based on game engine[C]//Proceedings of Annual Acis International Conference on Computer and Information Scie-nce.Wuahn:IEEE,2017:1-6. [6]YANG Y B,LIN H,ZHU Q.Content-Based 3D Model Retrie-val:A Survey[J].CHINESE JOURNAL OF COMPUTERS,2004,27(10):1297-1310.(in Chinese)杨育彬,林珲,朱庆.基于内容的三维模型检索综述[J].计算机学报,2004,27(10):1297-1310. [7]SU H,MAJI S,KALOGERAKIS E,et al.Multi-view Convolutional Neural Networks for 3D Shape Recognition[C]//Proceedings of International Conference on Computer Vision.Santiago:IEEE,2015:945-953. [8]FENG Y,ZHANG Z,ZHAO X,et al.GVCNN:Group-ViewConvolutional Neural Networks for 3D Shape Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:264-272. [9]KANEZAKI A,MATSUSHITA Y,NISHIDA Y,et al.Rota-tionNet:Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:5010-5019. [10]WU Z,SONG S,KHOSLA A,et al.3D ShapeNets:A deep representation for volumetric shapes[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1912-1920. [11]LI Y,PIRK S,SU H,et al.FPNN:Field Probing Neural Networks for 3D Data[C]//Proceedings of Neural Information Processing Systems.Barcelona:Curran Associates,2016:307-315. [12]QI C R,SU H,MO K,et al.PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:77-85. [13]QI C R,YI L,SU H,et al.PointNet++:Deep Hierarchical Feature Learning on Point Sets in a Metric Space[C]//Procee-dings of Neural Information Processing Systems.Long Beach:Curran Associates,2017:5099-5108. [14]FENG Y,FENG Y,YOU H,et al.MeshNet:Mesh Neural Network for 3D Shape Representation[C]//Proceedings of AAAI Conference on Artificial Intelligence.Honolulu:AAAI,2019. [15]TATARCHENKO M,DOSOVITSKIY A,BROX T,et al.Octree Generating Networks:Efficient Convolutional Architectures for High-resolution 3D Outputs[C]//Proceedings of International Conference on Computer Vision.Venice:IEEE,2017:2107-2115. [16]LI H S,SUN L,WU Y J,et al.Survey on Feature Extraction Techniques for Non-Rigid 3D Shape Retrieval[J].Journal of Software,2018,29(2):483-505.(in Chinese)李海生,孙莉,武玉娟,等.非刚性三维模型检索特征提取技术研究[J].软件学报,2018,29(2):483-505. [17]GUO Y L.Depth feature representation of 3d shape data[J].CCF Computer Vision Newsletter,2017(2):8-11.(in Chinese)郭裕兰.三维形状数据的深度特征表示[J].CCF计算机视觉专委简报,2017(2):8-11. [18]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [19]HINTON G E,OSINDERO S,TEH Y W,et al.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554. [20]HINTON G E,SALAKHUTDINOV R.Reducing the dimen-sionality of data with neural networks[J].Science,2006,313(5786):504-507. [21]RUMELHART D E,HINTON G E,WILLIAMS R J,et al.Learning representations by back-propagating errors[J].Nature,1988,323(6088):696-699. [22]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative Adversarial Nets[C]//Proceedings of Neural Information Processing Systems.Montreal:Curran Associates,2014:2672-2680. [23]ZOPH B,LE Q V.Searching for Activation Functions[C]//Proceedings of International Conference on Learning Representations.Vancouver,2018. [24]KRIZHEVSKY A,SUTSKEVER I,HINTON G E,et al.ImageNet Classification with Deep Convolutional Neural Networks[J].Neural Information Processing Systems,2012,141(5):1097-1105. [25]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//Proceedings of International Conference on Learning Representations.San Diego,2015. [26]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1-9. [27]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:770-778. [28]HUANG G,LIU Z,LAURENS V D M,et al.Densely Connec-ted Convolutional Networks[C]//Proceedings of IEEE Confe-rence on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:2261-2269. [29]YANG Y,ZHONG Z,SHEN T,et al.Convolutional NeuralNetworks with Alternately Updated Clique[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:2413-2422. [30]SUN K,XIAO B,LIU D,et al.HRNet:Deep High-Resolution Representation Learning for Human Pose Estimation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:5693-5703. [31]CHEN X,DUAN Y,HOUTHOOFT R,et al.InfoGAN:interpretable representation learning by information maximizing generative adversarial nets[C]//Proceedings of Neural Information Processing Systems.Barcelona:Curran Associates,2016:2180-2188. [32]LIU C,CHEN L C,SCHROFF F,et al.Auto-DeepLab:Hierarchical Neural Architecture Search for Semantic Image Segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:82-92. [33]TAN M,LE Q V.EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks[C]//Proceedings of International Conference on Machine Learning.Long Beach:ACM,2019. [34]IANDOLA F N,HAN S,MOSKEWICZ M W,et al.Squeeze-Net:AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size[C]//Proceedings of International Conference on Learning Representations.Toulon,2017. [35]SANDLER M,HOWARD A,ZHU M,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:4510-4520. [36]ZHANG X,ZHOU X,LIU M,et al.ShuffleNet:An Extremely Efficient Convolutional Neural Network for Mobile Devices[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:6848-6856. [37]XIE G,WANG J,ZHANG T,et al.Interleaved StructuredSparse Convolutional Neural Networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:8847-8856. [38]MEHTA S,RASTEGARI M,CASPI A,et al.ESPNet:Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation[C]//Proceedings of The European Conference on Computer Vision.Munich:Springer,2018:552-568. [39]GOYAL P,DOLLAR P,GIRSHICK R B,et al.Accurate,Large Minibatch SGD:Training ImageNet in 1 Hour[J].arXiv:Computer Vision and Pattern Recognition,2017. [40]KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[C]//Proceedings of International Conference on Learning Representations.San Diego,2015. [41]DUCHI J C,HAZAN E,SINGER Y,et al.Adaptive Subgradient Methods for Online Learning and Stochastic Optimization[J].Journal of Machine Learning Research,2011,12(7):2121-2159. [42]LUO L,XIONG Y,LIU Y,et al.Adaptive Gradient Methods with Dynamic Bound of Learning Rate[C]//Proceedings of International Conference on Learning Representations.New Orleans,2019. [43]FURUYA T,OHBUCHI R.Deep Aggregation of Local 3D Geometric Features for 3D Model Retrieval[C]//Proceedings of British Machine Vision Conference.York:BMVA Press,2016. [44]XIA Q,LI S,HAO A M,et al.Deep Learning for Digital Geometry Processing and Analysis:A Review[J].Journal of Computer Research and Development,2019,56(1):155-182.(in Chinese)夏清,李帅,郝爱民,等.基于深度学习的数字几何处理与分析技术研究进展[J].计算机研究与发展,2019,56(1):155-182. [45]KAZHDAN M M,FUNKHOUSER T A,RUSINKIEWICZ S,et al.Rotation invariant spherical harmonic representation of 3D shape descriptors[C]//Proceedings of Eurographics Symposium on Geometry Processing.Aachen:The Eurographics Association,2003:156-164. [46]CHEN D Y,TIAN X P,SHEN Y T,et al.On Visual Similarity Based 3D Model Retrieval[J].Computer Graphics Forum,2003,22(3):223-232. [47]MAHMOUDI M,SAPIRO G.Three-dimensional point cloudrecognition via distributions of geometric distances[J].Graphical Models,2009,71(1):22-31. [48]SUN J,OVSJANIKOV M,GUIBAS L.A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion[J].Computer Graphics Forum,2009,28(5):1383-1392. [49]AUBRY M,SCHLICKEWEI U,CREMERS D.The wave kernel signature:A quantum mechanical approach to shape analysis[C]//Proceedings of International Conference on Computer Vision Workshops.Barcelona:IEEE,2011:1626-1633. [50]KLOKOV R,LEMPITSKY V.Escape from Cells:Deep Kd-Networks for the Recognition of 3D Point Cloud Models[C]//Proceedings of International Conference on Computer Vision.Venice:IEEE,2017:863-872. [51]WANG Y,SUN Y,LIU Z,et al.Dynamic Graph CNN forLearning on Point Clouds[J].arXiv:Computer Vision and Pattern Recognition,2018. [52]JOHNS E,LEUTENEGGER S,DAVISON A J.Pairwise Decomposition of Image Sequences for Active Multi-View Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:3813-3822. [53]WANG C,SAMARI B,SIDDIQI K,et al.Local Spectral Graph Convolution for Point Set Feature Learning[C]//Proceedings of European Conference on Computer Vision.Munich:Springer,2018:56-71. [54]LI J,CHEN B M,LEE G H,et al.SO-Net:Self-Organizing Network for Point Cloud Analysis[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:9397-9406. [55]ZHAO Y,BIRDAL T,DENG H,et al.3D Point-Capsule Net-works[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:1009-1018. [56]LAN S,YU R,YU G,et al.Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:998-1008. [57]KATO H,USHIKU Y,HARADA T.Neural 3D Mesh Renderer[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:3907-3916. [58]GROUEIX T,FISHER M,KIM V G,et al.AtlasNet:A Papier-Ma^ché Approach to Learning 3D Surface Generation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:216-224. [59]ZHOU Y,ZENG F,QIAN J,et al.3D shape classification andretrieval based on polar view[J].Information Sciences,2019,474:205-220. [60]JIANG J,BAO D,CHEN Z,et al.MLVCNN:Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval[C]//Proceedings of AAAI Conference on Artificial Intelligence.Honolulu:AAAI,2019. [61]LI Y M,XUE K X,GAO Z,et al.3-D Model Retrieval Algorithm Based on Residual Network[J].COUMPUTER SCIENCE,2019,46(3):148-153.(in Chinese)李荫民,薛凯心,高赞,等.基于残差网络的三维模型检索算法[J].计算机科学,2019,46(3):148-153. [62]MATURANA D,SCHERER S.VoxNet:A 3D ConvolutionalNeural Network for real-time object recognition[C]//Proceedings of Intelligent Robots and Systems.Hamburg:IEEE,2015:922-928. [63]YANG J,WANG S,ZHOU P.Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network[J].Acta Optica Sinica,2019,39(4):314-324.(in Chinese)杨军,王顺,周鹏.基于深度体素卷积神经网络的三维模型识别分类[J].光学学报,2019,39(4):314-324. [64]WANG C,CHENG M,SOHEL F,et al.NormalNet:A voxel-based CNN for 3D object classification and retrieval[J].Neurocomputing,2019,323:139-147. [65]KUMAWAT S,RAMAN S.LP-3DCNN:Unveiling Local Phase in 3D Convolutional Neural Networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:4903-4912. [66]RIEGLER G,ULUSOY A O,GEIGER A,et al.OctNet:Learning Deep 3D Representations at High Resolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:6620-6629. [67]WANG P S,LIU Y,GUO Y X,et al.O-CNN:octree-based convolutional neural networks for 3D shape analysis[J].ACM Transactions on Graphics,2017,36(4):72:1-72:11. [68]ZHOU Y,ZENG F.2D compressive sensing and multi-feature fusion for effective 3D shape retrieval[J].Information Sciences,2017,409-410:101-120. [69]HEGDE V,ZADEH R.Fusionnet:3D Object Classification Using Multiple Data Representations[J].arXiv:Computer Vision and Pattern Recognition,2016. [70]YOU H,FENG Y,JI R,et al.PVNet:A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition[J].Acm Multimedia,2018:1310-1318. [71]YOU H,FENG Y,ZHAO X,et al.PVRNet:Point-View Relation Neural Network for 3D Shape Recognition[C]//Proceedings of AAAI Conference on Artificial Intelligence.Honolulu:AAAI,2019. [72]BOYER E,BRONSTEIN A M,BRONSTEIN M M,et al.SHREC 2011:robust feature detection and description benchmark[C]//Proceedings of Eurographics Conference on 3D Object Retrieval.Llandudno:The Eurographics Association,2011:71-78. [73]ZENG H,LIU Y,LIU J,et al.Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks[J].IEEE Access,2018,6:76087-76097. [74]LUCIANO L,HAMZA A B.Deep learning with geodesic moments for 3D shape classification[J].Pattern Recognition Letters,2018:182-190. [75]HAN Z,LIU Z,HAN J,et al.Unsupervised Learning of 3-D Local Features From Raw Voxels Based on a Novel Permutation Voxelization Strategy[J].IEEE Transactions on Systems,Man,and Cybernetics,2019,49(2):481-494. [76]SINHA A,BAI J,RAMANI K,et al.Deep Learning 3D Shape Surfaces Using Geometry Images[C]//Proceedings of European Conference on Computer Vision.Amsterdam:Springer,2016:223-240. [77]BAI J,SI Q L,QIN F W.3D Model Classification and Retrieval Based on CNN and Voting Scheme[J].Journal of Computer-Aided Design & Computer Graphics,2019,31(2):123-134.(in Chinese)白静,司庆龙,秦飞巍.基于卷积神经网络和投票机制的三维模型分类与检索[J].计算机辅助设计与图形学学报,2019,31(2):123-134. [78]BU S,WANG L,HAN P,et al.3D shape recognition and re-trieval based on multi-modality deep learning[J].Neurocomputing,2017,259(2017):183-193. [79]ZENG H,LIU Y,LI S,et al.Convolutional Neural NetworkBased Multi-feature Fusion for Non-rigid 3D Model Retrieval[J].Journal of Information Processing Systems,2018,14(1):176-190. [80]HE T,HUANG H,YI L,et al.GeoNet:Deep Geodesic Networks for Point Cloud Analysis[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:6888-6897. [81]YU L,LI X,FU C,et al.PU-Net:Point Cloud Upsampling Network[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:2790-2799. [82]PAQUET E,RIOUX M.Nefertiti:A Query by Content Software for Three-Dimensional Models Databases Management[C]//International Conference on Recent Advances in 3-D Digital Imaging and Modeling (3DIM '97).Ottawa,Ontario,Canada:DBLP,1997. [83]MIN P,HALDERMAN J A,KAZHDAN M M,et al.Early experiences with a 3D model search engine[C]//Proceedings of International Conference on 3D Web Technology.New York:ACM,2003:7-18. [84]SAGARA H,TAKANO S,OKADA Y,et al.3D Model Data Retrieval System Using KAZE Feature for Accepting 2D Image as Query[C]//Proceedings of International Conference on Complex,Intelligent,and Software Intensive Systems.Fukuoka:IEEE,2016:617-622. [85]SINTUNATA V,AOKI T.3D object retrieval system usingskewness database[C]//Proceedings of International Conference on Control,Decision and Information Technologies.St.Julian's:IEEE,2016:123-128. [86]KAO C.Shape-based 3D model retrieval system.International Journal of Computers and Applications.https://www.tandfonline.com/doi/abs/10.1080/1206212X.2016.1226541. [87]WU Z Q,ZHU Z M,LIU W,et al.Retrieving 3D Models from Institutional Repository[J].Data Analysis and Knowledge Discovery,2017,1(1):73-80.(in Chinese)吴志强,祝忠明,刘巍,等.机构知识库三维模型检索与展示技术研究与实践[J].数据分析与知识发现,2017,1(1):73-80. [88]LI H S,LAI L,CAI Q,et al.3D Model Storage and Shape Distribution in Hadoop Environment[J].Journal of Computer Research and Development,2014,51(S2):18-29.(in Chinese)李海生,赖龙,蔡强,等.Hadoop环境下三维模型的存储及形状分布特征提取[J].计算机研究与发展,2014,51(S2):18-29. [89]BIASOTTI S,CERRI A,BRONSTEIN A M,et al.Quantifying 3D Shape Similarity Using Maps:Recent Trends,Applications and Perspectives[C]//Proceedings of eurographics.Strasbourg:The Eurographics Association,2014:135-159. [90]TABIA H,LAGA H,PICARD D,et al.Covariance Descriptors for 3D Shape Matching and Retrieval[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE,2014:4185-4192. [91]LI B,LU Y,LI C,et al.A comparison of 3D shape retrievalmethods based on a large-scale benchmark supporting multimodal queries[J].Computer Vision and Image Understanding,2015,131:1-27. [92]WANG J,MA K,SINGH V K,et al.BodyPrint:Pose Invariant 3D Shape Matching of Human Bodies[C]//Proceedings of International Conference on Computer Vision.Santiago:IEEE,2015:1591-1599. [93]Princeton University.The Princeton shape benchmark[EB/OL].(2005-03-15) .http://shape.cs.princeton.edu/benchmark/. [94]SHILANE P,MIN P,KZAHDAN M,et al.The Princeton shape benchmark[C]//Proceedings of Conference on the Shape Modeling Applications.Genova:IEEE,2004. [95]PAN S J,YANG Q.A Survey on Transfer Learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359. [96]SUTTON R S,BARTO A G.Reinforcement Learning:An Introduction[J].IEEE Transactions on Neural Networks,1998,9(5):1054-1054. [97]HAN Z,LIU Z,HAN J,et al.Mesh Convolutional RestrictedBoltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes[J].IEEE Transactions on Neural Networks,2017,28(10):2268-2281. [98]WANG J T,ZHAO L,QI X B.Face Recognition Method Based on Adaptive 3D Morphable Model and Multiple Manifold Discriminant Analysis[J].Computer Science,2017,44(S1):232-235,239.(in Chinese)王渐韬,赵丽,齐兴斌.自适应三维形变模型结合流形分析的人脸识别方法[J].计算机科学,2017,44(S1):232-235,239. [99]RUSSAKOVSKY O,DENG J,SU H,et al.ImageNet LargeScale Visual Recognition Challenge[J].International Journal of Computer Vision,2015,115(3):211-252. [100]CHANG A X,FUNKHOUSER T A,GUIBAS L J,et al.ShapeNet:An Information-Rich 3D Model Repository[J].arXiv:1512.03012,2015. [101]DUAN P.Anisotropy Radial Basis Function Spatial Interpola-tion Model Research for 3D Spatial Field[J].Acta Geodaetica et Cartographica Sinica,2018,47(12):1696.(in Chinese)段平.三维空间场各向异性径向基函数空间插值模型研究[J].测绘学报,2018,47(12):1696. [102]WU J,ZHANG C,XUE T,et al.Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling[C]//Proceedings of Neural Information Processing Systems.Barcelona:Curran Associates,2016:82-90. [103]PENG Y X,QI J W,HUANG X.Current Research Status and Prospects on Multimedia Content Understanding[J].Journal of Computer Research and Development,2019,56(1):183-208.(in Chinese)彭宇新,綦金玮,黄鑫.多媒体内容理解的研究现状与展望[J].计算机研究与发展,2019,56(1):183-208. [104]ODENA A.Semi-Supervised Learning with Generative Adversarial Networks[J].arXiv:1606.01583,2016. [105]LI H S,WU Y J,ZHENG Y P,et al.A Survey of 3D Data Analysis and Understanding Based on Deep Learning.Chinese Journal of Computers,2019:1-25..http://kns.cnki.net/kcms/detail/11.1826.TP.20190709.1509.002.html.(in Chinese)李海生,武玉娟,郑艳萍,等.基于深度学习的三维数据分 析理解方法研究综述.计算机学报,2019:1-25.[2019-07-25].http://kns.cnki.net/kcms/detail/11.1826.TP.20190709.1509.002.html. |
[1] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
[2] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[3] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[4] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[5] | 宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053 |
[6] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[7] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
[8] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[9] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[10] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[11] | 王润安, 邹兆年. 基于物理操作级模型的查询执行时间预测方法 Query Performance Prediction Based on Physical Operation-level Models 计算机科学, 2022, 49(8): 49-55. https://doi.org/10.11896/jsjkx.210700074 |
[12] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
[13] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
[14] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[15] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
|