Computer Science ›› 2024, Vol. 51 ›› Issue (1): 243-251.doi: 10.11896/jsjkx.230300134
• Computer Graphics & Multimedia • Previous Articles Next Articles
ZHOU Wenhao, HU Hongtao, CHEN Xu, ZHAO Chunhui
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[1]HUANG T,WU K J,WANG D C,et al.Video Anomaly Detection Based on Improved Time Segmentation Network[J].Computer Engineering,2022,48(11):137-144. [2]FENG L J,ZHAO C H.Transfer increment for generalized zero-shot learning[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(6):2506-2520. [3]FENG L J,ZHAO C H,LI X.Bias-Eliminated Semantic Refinement for Any-Shot Learning[J].IEEE Transactions on Image Processing,2022,31:2229-2244. [4]储岳中,乔雨楠.多注意力结合光流的视频超分辨方法[J].重庆工商大学学报(自然科学版),2022,39(4):1-8. [5]ZHAO Y,DENG B,SHEN C,et al.Spatio-temporal autoencoder for video anomaly detection[C]//Proceedings of the 25th ACM International Conference on Multimedia.2017:1933-1941. [6]WANG X Z,CHE Z P,JIANG B,et al.Robust unsupervisedvideo anomaly detection by multipath frame prediction[J].ar-Xiv:2011.02763,2021. [7]ZHOU W H,LI Y X,ZHAO C H.Object-Guided and Motion-Refined Attention Network for Video Anomaly Detection[C]//2022 IEEE International Conference on Multimedia and Expo(ICME).2022:1-6. [8]LIU W,LUO W X,LIAN D Z,et al.Future frame prediction for anomaly detection-a new baseline[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6536-6545. [9]YE M C,PENG X J,GAN W H,et al.Anopcn:Video anomaly detection via deep predictive coding network[C]//Proceedings of the 27th ACM International Conference on Multimedia.2019:1805-1813. [10]CHAI Z,ZHAO C H,HUANG B.Multisource-refined transfer network for industrial fault diagnosis under domain and category inconsistencies[J].IEEE Transactions on Cybernetics,2021,52(9):9784-9796. [11]SONG P Y,ZHAO C H.Slow down to go better:A survey on slow feature analysis[J].IEEE Transactions on Neural Networks and Learning Systems,Early Access. [12]SULTANI W,CHEN C,SHAH M.Real-world anomaly detection in surveillance videos[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6479-6488. [13]LIU W,LUO W X,LI Z X,et al.Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies[C]//IJCAI.2019:3023-3030. [14]ZHONG J X,LI N N,KONG W J,et al.Graph convolutional label noise cleaner:Train a plug-and-play action classifier for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:1237-1246. [15]PURWANTO D,CHEN Y T,FANG W H.Dance with self-attention:A new look of conditional random fields on anomaly detection in videos[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:173-183. [16]TIAN Y,PANG G S,CHEN Y H,et al.Weakly-supervised vi-deo anomaly detection with robust temporal feature magnitude learning[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:4975-4986. [17]GONG D,LIU L Q,LE V,et al.Memorizing normality to detect anomaly:Memory-augmented deep autoencoder for unsupervised anomaly detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:1705-1714. [18]PARK H,NOH J,HAM B.Learning memory-guided normality for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:14372-14381. [19]LIU Z A,NIE Y W,LONG C J,et al.A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:13588-13597. [20]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems.2014:2672-2680. [21]AKCAY S,ATAPOUR-ABARGHOUEI A,BRECKON T P.Ganomaly:Semi-supervised anomaly detection via adversarial training[C]//Asian Conference on Computer Vision.2018:622-637. [22]HU H,GU J Y,ZHANG Z,et al.Relation networks for object detection[C]//Proceedings of the IEEE Conference on Compu-ter Vision and Pattern Recognition.2018:3588-3597. [23]WU P,LIU J,SHI Y J,et al.Not only look,but also listen:Learning multimodal violence detection under weak supervision[C]//European Conference on Computer Vision.2020:322-339. [24]ZHOU B,ANDONIAN A,OLIVA A,et al.Temporal relational reasoning in videos[C]//Proceedings of the European Confe-rence on Computer Vision(ECCV).2018:803-818. [25]WESTON J,CHOPRA S,BORDES A.Memory networks[J].arXiv:1410.3916,2014. [26]SUKHBAATAR S,WESTON J,FERGUS R.End-to-end me-mory networks[J].Advances in Neural Information Processing Systems,2015,28:1-9. [27]CARREIRA J,ZISSERMAN A,QUO V.action recognition? a new model and the kinetics dataset[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:6299-6308. [28]GIRDHAR R,CARREIRA J,DOERSCH C,et al.Video action transformer network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:244-253. [29]LUO W X,LIU W,GAO S H.A revisit of sparse coding based anomaly detection in stacked rnn framework[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:341-349. [30]WAN B,FANG Y M,XIA X,et al.Weakly supervised videoanomaly detection via center-guided discriminative learning[C]//2020 IEEE International Conference on Multimedia and Expo(ICME).2020:1-6. [31]FENG J C,HONG F T,ZHENG W S.Mist:Multiple instance self-training framework for video anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:14009-14018. [32]ZHANG J G,QING L Y,MIAO J.Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection[C]//2019 IEEE International Conference on Image Processing(ICIP).2019:4030-4034. [33]ZHU Y,NEWSAM S.Motion-aware feature for improved video anomaly detection[J].arXiv:1907.10211,2019. [34]VAN DER MAATEN L,HINTON G.Visualizing data using t-SNE[J].Journal of Machine Learning Research,2008,9(11):2579-2605. |
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