[1] Ibrahim M S,Muralidharan S,Deng Z,et al.A hierarchical deep temporal model for group activity recognition[A].Proceedings of CVPR[C].USA:IEEE,2016.1971-1980.
[2] Jain A,Zamir A R,Savarese S,et al.Structural-rnn:Deep learning on spatio-temporal graphs[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2016.5308-5317.
[3] Deng Z,Vahdat A,Hu H,et al.Structure inference machines:recurrent neural networks for analyzing relations in group activity recognition[A].Proceedings of CVPR[C].USA:IEEE,2016.4772-4781.
[4] Shu T,Todorovic S,Zhu S C.CERN:Confidence-energy recurrent network for group activity recognition[A].Proceedings of CVPR[C].USA:IEEE,2017.4255-4263.
[5] Biswas S,Gall J.Structural recurrent neural network (srnn)for group activity analysis[A]. IEEE Winter Conference on Applications of Computer Vision (WACV)[C].USA:IEEE,2018.1625-1632.
[6] Gammulle H,Denman S,Sridharan S,et al.Multi-Level Sequence GAN for Group Activity Recognition[A].Asian Conference on Computer Vision[C].Cham:Springer,2018.331-346.
[7] SHI Lei,ZHANG Yifan,CHENG Jian,LU Hanqing.Two-stream adaptive graph convolutional networks for skeleton-based action recognition[A].Proceedings of CVPR[C].USA:IEEE,2019.12026-12035.
[8] Wu J,Wang L,Wang L,et al.Learning actor relation graphs for group activity recognition[A].Proceedings of CVPR[C].USA:IEEE,2019.9964-9974.
[9] Ibrahim MS,Mori G.Hierarchical relational networks for group activity recognition and retrieval[A].European Conference on Computer Vision[C].USA:ECCV,2018.721-736.
[10] Yan R,Tang J,Shu X,et al.Participation-contributed temporal dynamic model for group activity recognition[A].ACM Multimedia Conference[C].USA:ACM,2018.1292-1300.
[11] Kong L,Qin J,Huang D,et al.Hierarchical attention and context modeling for group activity recognition[A].Proceedings of ICASSP[C].USA:IEEE,2018.1328-1332.
[12] Ramanathan V,Huang J,Abu-El-Haija S,et al.Detecting events and key actors in multi-person videos[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2016.3043-3053.
[13] 桑海峰,王传正,吕应宇,何大阔,刘晴.基于多信息流动卷积神经网络的行人再识别[J].电子学报,2019,47(2):351-357. SANG Hai-feng,WANG Chuan-zheng,LÜ Ying-yu,HE Da-kuo,LIU Qing.Person re-identification based on multi-information flow convolutional neural network[J].Acta Electronica Sinica,2019,47(2):351-357.(in Chinese)
[14] Danelljan M,Khan F,Felsberg M,et al.Accurate scale estimation for robust visual tracking[A].British Machine Vision Conference,Nottingham[C].[S.L.]:BMVA,2014.1-5.
[15] Russakovsky O,Deng J,Su H,et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-52.
[16] He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2016.770-778.
[17] 吕品,李全刚,柳厅文,宁振虎,王玉斌,时金桥,方滨兴.基于双向LSTM的误植域名滥用检测方法[J].电子学报,2018,46(9):2081-2086. LÜ Pin,LI Quan-gang,LIU Ting-wen,NING Zhen-hu,WANG Yu-bin,SHI Jin-qiao,FANG Bin-xing.Towards typo squatting abuse detection using bi-directional LSTM[J].Acta Electronica Sinica,2018,46(9):2081-2086.(in Chinese)
[18] Brox T,Bruhn A,Weickert J,et al.High accuracy optical flow estimation based on a theory for warping[A].European Conference on Computer Vision[C].Berlin:Springer-Verlag,2004.25-36.
[19] 罗会兰,王婵娟.行为识别中一种基于融合特征的改进VLAD编码方法[J].电子学报,2019,47(1):49-58. LUO Hui-lan,WANG Chan-juan.An improved VLAD coding method based on fusion feature in action recognition[J].Acta Electronica Sinica,2019,47(1):49-58.(in Chinese)
[20] Arevalo J,Solorio T,et al.Gated Multimodal Units for Information Fusion[M].London:ICLR,2017.0941-0643.
[21] Kingma D P,Ba J.Adam:A method for stochastic optimization[A].International Conference on Learning Representations (ICLR)[C].Ithaca,NY:arXiv.org,2014.13. |