Computer Science ›› 2020, Vol. 47 ›› Issue (4): 125-130.doi: 10.11896/jsjkx.190700163
• Computer Graphics & Multimedia • Previous Articles Next Articles
ZHOU Zi-qin, YAN Hua
CLC Number:
[1]WU Z,SONG S,KHOSLA A,et al.3D shapenets:A deep representation for volumetric shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1912-1920. [2]DENG J,DONG W,SOCHER R,et al.Imagenet:A large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.2009:248-255. [3]YU T,YAN J,WANG Y,et al.Generalizing graph matching beyond quadratic assignment model[C]//Advances in Neural Information Processing Systems.2018:853-863. [4]YANG Y,FENG C,SHEN Y,et al.Foldingnet:Point cloud auto-encoder via deep grid deformation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:206-215. [5]SHEN Y,FENG C,YANG Y,et al.Mining point cloud localstructures by kernel correlation and graph pooling[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:4548-4557. [6]QI C R,SU H,NIEβNER M,et al.Volumetric and multi-view cnns for object classification on 3D data[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:5648-5656. [7]JOHNS E,LEUTENEGGER S,DAVISON A J.Pairwise decomposition of image sequences for active multi-view recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:3813-3822. [8]HAN Z,SHANG M,LIU Y S,et al.View inter-prediction gan:Unsupervised representation learning for 3D shapes by learning global shape memories to support local view predictions[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:8376-8384. [9]SU H,MAJI S,KALOGERAKIS E,et al.Multi-view convolutional neural networks for 3D shape recognition[C]//Procee-dings of the IEEE International Conference on Computer Vision.2015:945-953. [10]SCHROFF F,KALENICHENKO D,PHILBIN J.Facenet:Aunified embedding for face recognition and clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:815-823. [11]WANG C,ZHANG X,LAN X.How to train triplet networks with 100k identities?[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1907-1915. [12]HE X,ZHOU Y,ZHOU Z,et al.Triplet-center loss for multi-view 3D object retrieval[C]//Proceedings of the IEEEConfe-rence on Computer Vision and Pattern Recognition.2018:1945-1954. [13]LONG M,WANG J.Learning multiple tasks with deep relationship networks[J].arXiv:1506.02117,2015. [14]BINGEL J,SØGAARD A.Identifying beneficial task relationsfor multi-task learning in deep neural networks[J].arXiv:1702.08303,2017. [15]LU Y,KUMAR A,ZHAI S,et al.Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:5334-5343. [16]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.2012:1097-1105. [17]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014. [18]HE K,ZHANG X,REN S,et al.Delving deep into rectifiers:Surpassing human-level performance on imagenet classification[C]//Proceedings of the IEEE international conference on computer vision.2015:1026-1034. |
[1] | DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65. |
[2] | ZHAO Kai, AN Wei-chao, ZHANG Xiao-yu, WANG Bin, ZHANG Shan, XIANG Jie. Intracerebral Hemorrhage Image Segmentation and Classification Based on Multi-taskLearning of Shared Shallow Parameters [J]. Computer Science, 2022, 49(4): 203-208. |
[3] | YANG Xiao-yu, YIN Kang-ning, HOU Shao-qi, DU Wen-yi, YIN Guang-qiang. Person Re-identification Based on Feature Location and Fusion [J]. Computer Science, 2022, 49(3): 170-178. |
[4] | SONG Long-ze, WAN Huai-yu, GUO Sheng-nan, LIN You-fang. Multi-task Spatial-Temporal Graph Convolutional Network for Taxi Idle Time Prediction [J]. Computer Science, 2021, 48(7): 112-117. |
[5] | LIU Xiao-long, HAN Fang, WANG Zhi-jie. Joint Question Answering Model Based on Knowledge Representation [J]. Computer Science, 2021, 48(6): 241-245. |
[6] | ZHOU Xiao-jin, XU Chen-ming, RUAN Tong. Multi-granularity Medical Entity Recognition for Chinese Electronic Medical Records [J]. Computer Science, 2021, 48(4): 237-242. |
[7] | ZHANG Chun-yun, QU Hao, CUI Chao-ran, SUN Hao-liang, YIN Yi-long. Process Supervision Based Sequence Multi-task Method for Legal Judgement Prediction [J]. Computer Science, 2021, 48(3): 227-232. |
[8] | WANG Ti-shuang, LI Pei-feng, ZHU Qiao-ming. Chinese Implicit Discourse Relation Recognition Based on Data Augmentation [J]. Computer Science, 2021, 48(10): 85-90. |
[9] | PAN Zu-jiang, LIU Ning, ZHANG Wei, WANG Jian-yong. MTHAM:Multitask Disease Progression Modeling Based on Hierarchical Attention Mechanism [J]. Computer Science, 2020, 47(9): 185-189. |
[10] | GENG Lei-lei, CUI Chao-ran, SHI Cheng, SHEN Zhen, YIN Yi-long, FENG Shi-hong. Social Image Tag and Group Joint Recommendation Based on Deep Multi-task Learning [J]. Computer Science, 2020, 47(12): 177-182. |
[11] | CHEN Xun-min, YE Shu-han, ZHAN Rui. Crowd Counting Model of Convolutional Neural Network Based on Multi-task Learning and Coarse to Fine [J]. Computer Science, 2020, 47(11A): 183-187. |
[12] | GAO Li-jian,MAO Qi-rong. Environment-assisted Multi-task Learning for Polyphonic Acoustic Event Detection [J]. Computer Science, 2020, 47(1): 159-164. |
[13] | WU Liang-qing, ZHANG Dong, LI Shou-shan, CHEN Ying. Multi-modal Emotion Recognition Approach Based on Multi-task Learning [J]. Computer Science, 2019, 46(11): 284-290. |
[14] | MENG Hao-hua LI Guo-zheng (School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China). [J]. Computer Science, 2008, 35(10): 186-187. |
|