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Konrad, J.,
Jodoin, P.M.,
Video Anomaly Identification,
SPMag(27), No. 5, 2010, pp. 18-33.
IEEE DOI
1003
BibRef
Cheng, K.W.[Kai-Wen],
Chen, Y.T.[Yie-Tarng],
Fang, W.H.[Wen-Hsien],
Gaussian Process Regression-Based Video Anomaly Detection and
Localization With Hierarchical Feature Representation,
IP(24), No. 12, December 2015, pp. 5288-5301.
IEEE DOI
1512
BibRef
Earlier:
Video anomaly detection and localization using hierarchical feature
representation and Gaussian process regression,
CVPR15(2909-2917)
IEEE DOI
1510
Gaussian processes
BibRef
Blair, C.G.,
Robertson, N.M.,
Video Anomaly Detection in Real Time on a Power-Aware Heterogeneous
Platform,
CirSysVideo(26), No. 11, November 2016, pp. 2109-2122.
IEEE DOI
1609
Algorithm design and analysis
BibRef
Leyva, R.[Roberto],
Sanchez, V.[Victor],
Li, C.T.[Chang-Tsun],
Video Anomaly Detection With Compact Feature Sets for Online
Performance,
IP(26), No. 7, July 2017, pp. 3463-3478.
IEEE DOI
1706
Cameras, Data mining, Feature extraction, Optical imaging, Training,
Video anomaly detection, online processing, video surveillance
BibRef
Wang, Z.G.[Zhi-Guo],
Yang, Z.L.[Zhong-Liang],
Zhang, Y.J.[Yu-Jin],
A promotion method for generation error-based video anomaly detection,
PRL(140), 2020, pp. 88-94.
Elsevier DOI
2012
Anomaly detection, Block-level, Generation error, Surveillance video
BibRef
Zavrtanik, V.[Vitjan],
Kristan, M.[Matej],
Skocaj, D.[Danijel],
Reconstruction by inpainting for visual anomaly detection,
PR(112), 2021, pp. 107706.
Elsevier DOI
2102
Anomaly detection, Video anomaly detection, Inpainting, CNN
BibRef
Guo, A.[Aibin],
Guo, L.J.[Li-Jun],
Zhang, R.[Rong],
Wang, Y.[Yirui],
Gao, S.[Shangce],
Self-trained prediction model and novel anomaly score mechanism for
video anomaly detection,
IVC(119), 2022, pp. 104391.
Elsevier DOI
2202
Anomaly detection, Unsupervised method, Memory module,
Reconstruction, Self-training mechanism
BibRef
Ramachandra, B.[Bharathkumar],
Jones, M.J.[Michael J.],
Vatsavai, R.R.[Ranga Raju],
A Survey of Single-Scene Video Anomaly Detection,
PAMI(44), No. 5, May 2022, pp. 2293-2312.
IEEE DOI
2204
Anomaly detection, Computational modeling, Cameras, Training,
Buildings, Legged locomotion, Feeds, Video anomaly detection,
surveillance
BibRef
Cho, M.[MyeongAh],
Kim, T.[Taeoh],
Kim, W.J.[Woo Jin],
Cho, S.[Suhwan],
Lee, S.Y.[Sang-Youn],
Unsupervised video anomaly detection via normalizing flows with
implicit latent features,
PR(129), 2022, pp. 108703.
Elsevier DOI
2206
Video anomaly detection, Surveillance system, AutoEncoder, Normalizing flow
BibRef
Jia, D.Y.[Di-Yang],
Zhang, X.[Xiao],
Zhou, J.T.Y.[Joey Tian-Yi],
Lai, P.[Pan],
Wei, Y.F.[Yi-Fei],
Dynamic thresholding for video anomaly detection,
IET-IPR(16), No. 11, 2022, pp. 2973-2982.
DOI Link
2208
BibRef
Chen, H.Y.[Hao-Yang],
Mei, X.[Xue],
Ma, Z.Y.[Zhi-Yuan],
Wu, X.H.[Xin-Hong],
Wei, Y.C.[Ya-Chuan],
Spatial-temporal graph attention network for video anomaly detection,
IVC(131), 2023, pp. 104629.
Elsevier DOI
2303
Video anomaly detection, Multiple instance learning,
Graph convolutional network, Multi-head graph attention
BibRef
Sun, Q.Y.[Qi-Yue],
Yang, Y.[Yang],
Unsupervised video anomaly detection based on multi-timescale
trajectory prediction,
CVIU(227), 2023, pp. 103615.
Elsevier DOI
2301
Video anomaly detection, Multi-timescale,
Trajectory prediction, Velocity calculation module
BibRef
Wang, L.[Le],
Tian, J.W.[Jun-Wen],
Zhou, S.P.[San-Ping],
Shi, H.Y.[Hao-Yue],
Hua, G.[Gang],
Memory-augmented appearance-motion network for video anomaly
detection,
PR(138), 2023, pp. 109335.
Elsevier DOI
2303
Anomaly detection, Memory network, Autoencoder, Abnormal events
BibRef
Cheng, K.[Kai],
Liu, Y.[Yang],
Zeng, X.H.[Xin-Hua],
Learning Graph Enhanced Spatial-Temporal Coherence for Video Anomaly
Detection,
SPLetters(30), 2023, pp. 314-318.
IEEE DOI
2304
Optical signal processing, Decoding, Benchmark testing,
Task analysis, Optical computing, Coherence, Predictive models,
graph network
BibRef
Zhao, M.Y.[Meng-Yang],
Liu, Y.[Yang],
Liu, J.[Jing],
Zeng, X.H.[Xin-Hua],
Exploiting Spatial-temporal Correlations for Video Anomaly Detection,
ICPR22(1727-1733)
IEEE DOI
2212
Visualization, Correlation, Benchmark testing,
Generative adversarial networks,
spatial-temporal consistency
BibRef
Park, J.[Jaeyoo],
Kim, J.[Junha],
Han, B.H.[Bo-Hyung],
End-to-end learning for weakly supervised video anomaly detection
using Absorbing Markov Chain,
CVIU(236), 2023, pp. 103798.
Elsevier DOI
2310
BibRef
Earlier:
Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision,
ACCV20(V:514-529).
Springer DOI
2103
Anomaly Detection, Weakly-supervised Learning, Absorbing Markov Chain
BibRef
Shao, W.H.[Wen-Hao],
Xiao, R.[Ruliang],
Rajapaksha, P.[Praboda],
Wang, M.Z.[Meng-Zhu],
Crespi, N.[Noel],
Luo, Z.G.[Zhi-Gang],
Minerva, R.[Roberto],
Video anomaly detection with NTCN-ML:
A novel TCN for multi-instance learning,
PR(143), 2023, pp. 109765.
Elsevier DOI
2310
Video process, Anomaly detection,
Feature extraction, Temporal convolutional network, Deep learning
BibRef
Zhong, Y.H.[Yuan-Hong],
Hu, Y.T.[Yong-Ting],
Tang, P.L.[Pan-Liang],
Wang, H.[Heng],
Associative Memory with Spatio-Temporal Enhancement for Video Anomaly
Detection,
SPLetters(30), 2023, pp. 1212-1216.
IEEE DOI
2310
BibRef
Tang, J.[Jun],
Wang, Z.T.[Zhen-Tao],
Hao, G.Y.[Guan-Yu],
Wang, K.[Ke],
Zhang, Y.[Yan],
Wang, N.[Nian],
Liang, D.[Dong],
SAE-PPL: Self-guided attention encoder with prior knowledge-guided
pseudo labels for weakly supervised video anomaly detection,
JVCIR(97), 2023, pp. 103967.
Elsevier DOI
2312
Weakly supervised video anomaly detection, Self-training,
Multiple instance learning, Attention mechanism
BibRef
Wu, P.H.[Pei-Hao],
Wang, W.Q.[Wen-Qian],
Chang, F.[Faliang],
Liu, C.S.[Chun-Sheng],
Wang, B.[Bin],
DSS-Net: Dynamic Self-Supervised Network for Video Anomaly Detection,
MultMed(26), 2024, pp. 2124-2136.
IEEE DOI
2402
Feature extraction, Anomaly detection, Hidden Markov models,
Task analysis, Generators, Generative adversarial networks,
self-supervised learning
BibRef
Singh, R.[Rituraj],
Sethi, A.[Anikeit],
Saini, K.[Krishanu],
Saurav, S.[Sumeet],
Tiwari, A.[Aruna],
Singh, S.[Sanjay],
CVAD-GAN: Constrained video anomaly detection via generative
adversarial network,
IVC(143), 2024, pp. 104950.
Elsevier DOI Code:
WWW Link.
2403
Video anomaly detection, Adversarial learning,
Surveillance video, Generative adversarial network (GAN)
BibRef
Cao, C.Q.[Cong-Qi],
Lu, Y.[Yue],
Zhang, Y.N.[Yan-Ning],
Context Recovery and Knowledge Retrieval: A Novel Two-Stream
Framework for Video Anomaly Detection,
IP(33), 2024, pp. 1810-1825.
IEEE DOI
2403
Streams, Testing, Feature extraction, Anomaly detection,
Predictive models, Object detection,
two-stream framework
BibRef
Majhi, S.[Snehashis],
Dai, R.[Rui],
Kong, Q.[Quan],
Garattoni, L.[Lorenzo],
Francesca, G.[Gianpiero],
Brémond, F.[François],
Human-Scene Network: A novel baseline with self-rectifying loss for
weakly supervised video anomaly detection,
CVIU(241), 2024, pp. 103955.
Elsevier DOI
2403
Video anomaly detection, Weakly-supervised learning
BibRef
Liu, H.[Hao],
He, L.J.[Li-Jun],
Zhang, M.[Miao],
Li, F.[Fan],
VADiffusion: Compressed Domain Information Guided Conditional
Diffusion for Video Anomaly Detection,
CirSysVideo(34), No. 9, September 2024, pp. 8398-8411.
IEEE DOI Code:
WWW Link.
2410
Anomaly detection, Image reconstruction, Predictive models,
Vectors, Optical flow, Decoding, Feature extraction, diffusion
BibRef
Zhang, D.J.[De-Jun],
Fang, W.B.[Wen-Bo],
Liu, Y.H.[Yu-Hang],
Lyu, Z.[Zirong],
Xiong, C.[Chen],
Wang, Z.[Zhan],
Two-stage video anomaly detection based on dual-stream networks and
multi-instance learning,
IET-IPR(18), No. 14, 2024, pp. 4843-4851.
DOI Link
2501
convolutional neural nets, feature extraction,
learning (artificial intelligence), object detection, video signal processing
BibRef
Zhou, Y.X.[Yi-Xuan],
Qu, Y.[Yi],
Xu, X.[Xing],
Shen, F.M.[Fu-Min],
Song, J.K.[Jing-Kuan],
Shen, H.T.[Heng Tao],
BatchNorm-Based Weakly Supervised Video Anomaly Detection,
CirSysVideo(34), No. 12, December 2024, pp. 13642-13654.
IEEE DOI Code:
WWW Link.
2501
Vectors, Anomaly detection, Annotations,
Training, Noise, Feature extraction, Batch normalization,
weakly supervised learning
BibRef
Shen, G.D.[Guo-Dong],
Ouyang, Y.Q.[Yu-Qi],
Lu, J.[Junru],
Yang, Y.X.[Yi-Xuan],
Sanchez, V.[Victor],
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework
for Enhanced Single- and Multi-Task Approaches,
IP(33), 2024, pp. 6865-6880.
IEEE DOI Code:
WWW Link.
2501
Transformers, Multitasking, Pipelines, Decoding,
Bidirectional control, Benchmark testing, Training,
multi-task
BibRef
Ahn, S.[Sunghyun],
Jo, Y.[Youngwan],
Lee, K.[Kijung],
Park, S.[Sanghyun],
Videopatchcore: An Effective Method to Memorize Normality for Video
Anomaly Detection,
ACCV24(III: 312-328).
Springer DOI
2412
BibRef
Tran, C.D.[Chi Dai],
Pham, L.H.[Long Hoang],
Tran, D.N.N.[Duong Nguyen-Ngoc],
Ho, Q.P.N.[Quoc Pham-Nam],
Jeon, J.W.[Jae Wook],
Dual Memory Networks Guided Reverse Distillation for Unsupervised
Anomaly Detection,
ACCV24(VI: 361-378).
Springer DOI
2412
BibRef
Shi, H.Y.[Hao-Yue],
Wang, L.[Le],
Zhou, S.P.[San-Ping],
Hua, G.[Gang],
Tang, W.[Wei],
Learning Anomalies with Normality Prior for Unsupervised Video Anomaly
Detection,
ECCV24(VI: 163-180).
Springer DOI
2412
BibRef
Nie, Y.W.[Yong-Wei],
Huang, H.[Hao],
Long, C.J.[Cheng-Jiang],
Zhang, Q.[Qing],
Maji, P.[Pradipta],
Cai, H.M.[Hong-Min],
Interleaving One-class and Weakly-supervised Models with Adaptive
Thresholding for Unsupervised Video Anomaly Detection,
ECCV24(XXX: 449-467).
Springer DOI
2412
BibRef
Jain, Y.[Yashika],
Dabouei, A.[Ali],
Xu, M.[Min],
Cross-domain Learning for Video Anomaly Detection with Limited
Supervision,
ECCV24(XXX: 468-484).
Springer DOI
2412
BibRef
Yao, X.C.[Xin-Cheng],
Li, R.[Ruoqi],
Qian, Z.F.[Ze-Feng],
Wang, L.[Lu],
Zhang, C.Y.[Chong-Yang],
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified
Anomaly Detection,
ECCV24(XXXII: 92-108).
Springer DOI
2412
BibRef
Liu, C.[Chieh],
Chu, Y.M.[Yu-Min],
Hsieh, T.I.[Ting-I],
Chen, H.T.[Hwann-Tzong],
Liu, T.L.[Tyng-Luh],
Learning Diffusion Models for Multi-view Anomaly Detection,
ECCV24(XXXIII: 328-345).
Springer DOI
2412
BibRef
Fucka, M.[Matic],
Zavrtanik, V.[Vitjan],
Skocaj, D.[Danijel],
Transfusion: A Transparency-based Diffusion Model for Anomaly
Detection,
ECCV24(XXXV: 91-108).
Springer DOI
2412
BibRef
Qi, F.[Fan],
Pan, R.J.[Rui-Jie],
Zhang, H.W.[Huai-Wen],
Xu, C.S.[Chang-Sheng],
Fedvad: Enhancing Federated Video Anomaly Detection with GPT-driven
Semantic Distillation,
ECCV24(LIII: 234-251).
Springer DOI
2412
BibRef
McIntosh, D.[Declan],
Albu, A.B.[Alexandra Branzan],
Unsupervised, Online and On-the-fly Anomaly Detection for
Non-stationary Image Distributions,
ECCV24(LXI: 428-445).
Springer DOI
2412
BibRef
Yang, Z.W.[Zhi-Wei],
Liu, J.[Jing],
Wu, P.[Peng],
Text Prompt with Normality Guidance for Weakly Supervised Video
Anomaly Detection,
CVPR24(18899-18908)
IEEE DOI
2410
Visualization, Adaptation models, Accuracy, Limiting,
Benchmark testing, Reliability engineering, self-training
BibRef
Chen, J.X.[Jun-Xi],
Li, L.[Liang],
Su, L.[Li],
Zha, Z.J.[Zheng-Jun],
Huang, Q.M.[Qing-Ming],
Prompt-Enhanced Multiple Instance Learning for Weakly Supervised
Video Anomaly Detection,
CVPR24(18319-18329)
IEEE DOI Code:
WWW Link.
2410
Training, Couplings, Annotations, Semantics, Detectors,
Benchmark testing, Feature extraction, Video Anomaly Detection
BibRef
Wu, P.[Peng],
Zhou, X.[Xuerong],
Pang, G.S.[Guan-Song],
Sun, Y.[Yujia],
Liu, J.[Jing],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
Open-Vocabulary Video Anomaly Detection,
CVPR24(18297-18307)
IEEE DOI
2410
Training, Computational modeling, Large language models, Semantics,
Buildings, Benchmark testing, video anomaly detection,
pre-trained large models
BibRef
Zhang, M.H.[Meng-Hao],
Wang, J.Y.[Jing-Yu],
Qi, Q.[Qi],
Sun, H.F.[Hai-Feng],
Zhuang, Z.[Zirui],
Ren, P.F.[Peng-Fei],
Ma, R.L.[Rui-Long],
Liao, J.X.[Jian-Xin],
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal
Representation Learning,
CVPR24(17385-17394)
IEEE DOI
2410
Representation learning, Location awareness, Estimation,
Contrastive learning, Feature extraction,
representation learning
BibRef
Ristea, N.C.[Nicolae-Catalin],
Croitoru, F.A.[Florinel-Alin],
Ionescu, R.T.[Radu Tudor],
Popescu, M.[Marius],
Khan, F.S.[Fahad Shahbaz],
Shah, M.[Mubarak],
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly
Detectors,
CVPR24(15984-15995)
IEEE DOI Code:
WWW Link.
2410
Training, Event detection, Training data, Detectors,
Data augmentation, Data models
BibRef
Zanella, L.[Luca],
Menapace, W.[Willi],
Mancini, M.[Massimiliano],
Wang, Y.M.[Yi-Ming],
Ricci, E.[Elisa],
Harnessing Large Language Models for Training-Free Video Anomaly
Detection,
CVPR24(18527-18536)
IEEE DOI
2410
Training, Large language models, Surveillance, Refining, Estimation,
Data collection, Turning, video anomaly detection,
vision-language models
BibRef
Du, H.[Hang],
Zhang, S.C.[Si-Cheng],
Xie, B.[Binzhu],
Nan, G.[Guoshun],
Zhang, J.Y.[Jia-Yang],
Xu, J.[Junrui],
Liu, H.[Hangyu],
Leng, S.[Sicong],
Liu, J.M.[Jiang-Ming],
Fan, H.[Hehe],
Huang, D.[Dajiu],
Feng, J.[Jing],
Chen, L.[Linli],
Zhang, C.[Can],
Li, X.[Xuhuan],
Zhang, H.[Hao],
Chen, J.H.[Jian-Hang],
Cui, Q.[Qimei],
Tao, X.F.[Xiao-Feng],
Uncovering what, why and How: A Comprehensive Benchmark for Causation
Understanding of Video Anomaly,
CVPR24(18793-18803)
IEEE DOI Code:
WWW Link.
2410
Measurement, Annotations, Surveillance, Natural languages,
Benchmark testing, Traffic control,
Large Language Model
BibRef
Micorek, J.[Jakub],
Possegger, H.[Horst],
Narnhofer, D.[Dominik],
Bischof, H.[Horst],
Kozinski, M.[Mateusz],
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching
for Video Anomaly Detection,
CVPR24(18868-18877)
IEEE DOI
2410
Noise, Neural networks, Noise reduction, Training data, Detectors,
Feature extraction, Vectors, anomaly detection,
frame-centric
BibRef
Ghadiya, A.[Ayush],
Kar, P.[Purbayan],
Chudasama, V.[Vishal],
Wasnik, P.[Pankaj],
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised
Video Anomaly Detection,
MULA24(1965-1974)
IEEE DOI
2410
Visualization, Adaptation models, Accuracy, Attention mechanisms,
Computational modeling, Multi-Modal, Fusion Mechanism,
Hyperbolic Graph Attention
BibRef
Rai, A.K.[Ayush K.],
Krishna, T.[Tarun],
Hu, F.[Feiyan],
Drimbarean, A.[Alexandru],
McGuinness, K.[Kevin],
Smeaton, A.F.[Alan F.],
O'Connor, N.E.[Noel E.],
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation:
A Unified Approach,
VAND24(3887-3899)
IEEE DOI
2410
Training, Semantics, Training data, Optical distortion, Distortion,
Image reconstruction, Video Anomaly Detection
BibRef
Lappas, D.[Demetris],
Argyriou, V.[Vasileios],
Makris, D.[Dimitrios],
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video
Anomaly Detection,
VAND24(3961-3970)
IEEE DOI Code:
WWW Link.
2410
Training, Adaptation models, Technological innovation, Accuracy,
Refining, Video Anomaly Detection, Pseudo Anomalies,
Distinction Loss
BibRef
Singh, A.[Ashish],
Jones, M.J.[Michael J.],
Learned-Miller, E.G.[Erik G.],
Tracklet-based Explainable Video Anomaly Localization,
VAND24(3992-4001)
IEEE DOI
2410
Location awareness, Training, Tracking, Video sequences,
Object detection, Trajectory,
tracklets
BibRef
Yang, Z.Y.[Zheng-Ye],
Radke, R.J.[Richard J.],
Context-aware Video Anomaly Detection in Long-Term Datasets,
VAND24(4002-4011)
IEEE DOI
2410
Schedules, Target tracking, Contrastive learning,
Benchmark testing, Video Anomaly Detection,
Long-term Surveillance
BibRef
Yao, S.[Shanle],
Noghre, G.A.[Ghazal Alinezhad],
Pazho, A.D.[Armin Danesh],
Tabkhi, H.[Hamed],
Evaluating the Effectiveness of Video Anomaly Detection in the Wild
Online Learning and Inference for Real-world Deployment,
ABAW24(4832-4841)
IEEE DOI
2410
Adaptation models, Surveillance, Streaming media, Data models,
Robustness, Real-time systems, Research initiatives, Anomaly Detection
BibRef
Hafeez, M.A.[Muhammad Adeel],
Javed, S.[Sajid],
Madden, M.[Michael],
Ullah, I.[Ihsan],
Unsupervised End-to-End Transformer based approach for Video Anomaly
Detection,
IVCNZ23(1-7)
IEEE DOI
2403
Training, Transfer learning, Transformers, Feature extraction,
Generators, Task analysis, Anomaly detection
BibRef
Chen, W.L.[Wei-Ling],
Ma, K.T.[Keng Teck],
Yew, Z.J.[Zi Jian],
Hur, M.[Minhoe],
Khoo, D.A.A.[David Aik-Aun],
TEVAD: Improved video anomaly detection with captions,
ODRUM23(5549-5559)
IEEE DOI
2309
BibRef
Kobayashi, S.[Shimpei],
Hizukuri, A.[Akiyoshi],
Nakayama, R.[Ryohei],
Video Anomaly Detection Using Encoder-Decoder Networks with Video
Vision Transformer and Channel Attention Blocks,
MVA23(1-4)
DOI Link
2403
Pedestrians, Surveillance, Receivers, Cameras, Transformers,
Motion pictures, Safety
BibRef
Yan, C.[Cheng],
Zhang, S.Y.[Shi-Yu],
Liu, Y.[Yang],
Pang, G.S.[Guan-Song],
Wang, W.J.[Wen-Jun],
Feature Prediction Diffusion Model for Video Anomaly Detection,
ICCV23(5504-5514)
IEEE DOI
2401
BibRef
Fioresi, J.[Joseph],
Dave, I.R.[Ishan Rajendrakumar],
Shah, M.[Mubarak],
TeD-SPAD: Temporal Distinctiveness for Self-supervised
Privacy-preservation for video Anomaly Detection,
ICCV23(13552-13563)
IEEE DOI
2401
BibRef
Flaborea, A.[Alessandro],
Collorone, L.[Luca],
d'Amely-di Melendugno, G.M.[Guido Maria],
d'Arrigo, S.[Stefano],
Prenkaj, B.[Bardh],
Galasso, F.[Fabio],
Multimodal Motion Conditioned Diffusion Model for Skeleton-based
Video Anomaly Detection,
ICCV23(10284-10295)
IEEE DOI
2401
BibRef
Tur, A.O.[Anil Osman],
Dall'Asen, N.[Nicola],
Beyan, C.[Cigdem],
Ricci, E.[Elisa],
Unsupervised Video Anomaly Detection with Diffusion Models Conditioned
on Compact Motion Representations,
CIAP23(II:49-62).
Springer DOI
2312
BibRef
Aich, A.[Abhishek],
Peng, K.C.[Kuan-Chuan],
Roy-Chowdhury, A.K.[Amit K.],
Cross-Domain Video Anomaly Detection without Target Domain Adaptation,
WACV23(2578-2590)
IEEE DOI
2302
Measurement, Training, Representation learning, Adaptation models,
Image color analysis, Training data, Predictive models
BibRef
Joo, H.K.[Hyekang Kevin],
Vo, K.[Khoa],
Yamazaki, K.[Kashu],
Le, N.[Ngan],
CLIP-TSA: Clip-Assisted Temporal Self-Attention for Weakly-Supervised
Video Anomaly Detection,
ICIP23(3230-3234)
IEEE DOI Code:
WWW Link.
2312
BibRef
Tur, A.O.[Anil Osman],
Dall'Asen, N.[Nicola],
Beyan, C.[Cigdem],
Ricci, E.[Elisa],
Exploring Diffusion Models for Unsupervised Video Anomaly Detection,
ICIP23(2540-2544)
IEEE DOI
2312
BibRef
Zhang, C.[Chen],
Li, G.R.[Guo-Rong],
Qi, Y.[Yuankai],
Wang, S.H.[Shu-Hui],
Qing, L.Y.[Lai-Yun],
Huang, Q.M.[Qing-Ming],
Yang, M.H.[Ming-Hsuan],
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly
Supervised Video Anomaly Detection,
CVPR23(16271-16280)
IEEE DOI
2309
BibRef
Cao, C.Q.[Cong-Qi],
Lu, Y.[Yue],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
A New Comprehensive Benchmark for Semi-supervised Video Anomaly
Detection and Anticipation,
CVPR23(20392-20401)
IEEE DOI
2309
BibRef
Sun, S.Y.[Sheng-Yang],
Gong, X.J.[Xiao-Jin],
Hierarchical Semantic Contrast for Scene-aware Video Anomaly
Detection,
CVPR23(22846-22856)
IEEE DOI
2309
BibRef
Xiang, T.[Tiange],
Zhang, Y.X.[Yi-Xiao],
Lu, Y.Y.[Yong-Yi],
Yuille, A.L.[Alan L.],
Zhang, C.Y.[Chao-Yi],
Cai, W.D.[Wei-Dong],
Zhou, Z.[Zongwei],
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection,
CVPR23(23890-23901)
IEEE DOI
2309
BibRef
Baradaran, M.[Mohammad],
Bergevin, R.[Robert],
Multi-Task Learning based Video Anomaly Detection with Attention,
VAND23(2886-2896)
IEEE DOI
2309
BibRef
Flaborea, A.[Alessandro],
Prenkaj, B.[Bardh],
Munjal, B.[Bharti],
Sterpa, M.A.[Marco Aurelio],
Aragona, D.[Dario],
Podo, L.[Luca],
Galasso, F.[Fabio],
Are we certain it's anomalous?,
VAND23(2897-2907)
IEEE DOI
2309
BibRef
Lee, T.Y.[Teng-Yok],
Nagai, Y.[Yusuke],
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VAND23(1-9)
IEEE DOI
2309
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VAND23(2937-2947)
IEEE DOI
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Anomaly Detection with Domain Adaptation,
VAND23(2958-2967)
IEEE DOI
2309
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Liu, Z.[Zuhao],
Wu, X.M.[Xiao-Ming],
Zheng, D.[Dian],
Lin, K.Y.[Kun-Yu],
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Feature Mapping,
CVPR23(24500-24510)
IEEE DOI
2309
BibRef
Tien, T.D.[Tran Dinh],
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Tran, N.H.[Nguyen Hoang],
Huy, T.D.[Ta Duc],
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Nguyen, C.D.T.[Chanh D. Tr.],
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CVPR23(24511-24520)
IEEE DOI
2309
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Lv, H.[Hui],
Yue, Z.Q.[Zhong-Qi],
Sun, Q.[Qianru],
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CVPR23(8022-8031)
IEEE DOI
2309
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Ouyang, Y.Q.[Yu-Qi],
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Sanchez, V.[Victor],
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2304
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Wang, Y.L.[Yun-Long],
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SSSPR22(84-93).
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2301
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2301
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Object Class Aware Video Anomaly Detection through Image Translation,
CRV22(90-97)
IEEE DOI
2301
Image segmentation, Motion segmentation, Semantics,
Benchmark testing, Task analysis, Anomaly detection, Robots,
semi-supervised learning
BibRef
Wu, J.C.[Jhih-Ciang],
Hsieh, H.Y.[He-Yen],
Chen, D.J.[Ding-Jie],
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Self-supervised Sparse Representation for Video Anomaly Detection,
ECCV22(XIII:729-745).
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2211
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Zhu, Y.S.[Yuan-Sheng],
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2211
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Liu, Z.[Zhian],
Nie, Y.W.[Yong-Wei],
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Li, G.Q.[Gui-Qing],
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow
Reconstruction and Flow-Guided Frame Prediction,
ICCV21(13568-13577)
IEEE DOI
2203
Image motion analysis, Correlation, Codes, Data preprocessing,
Memory modules, Reconstruction algorithms,
Motion and tracking
BibRef
Zhu, Y.Z.[Ye-Zhou],
Wang, J.Z.[Jian-Zhu],
Zhang, J.[Jing],
Li, Q.Y.[Qing-Yong],
A Two-Stage Autoencoder for Visual Anomaly Detection,
ICIP21(1869-1873)
IEEE DOI
2201
Measurement, Visualization, Decoding, Image reconstruction,
Anomaly detection, Autoencoder, RotNet, Anomaly Detection
BibRef
Feng, J.C.[Jia-Chang],
Hong, F.T.[Fa-Ting],
Zheng, W.S.[Wei-Shi],
MIST: Multiple Instance Self-Training Framework for Video Anomaly
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CVPR21(14004-14013)
IEEE DOI
2111
Annotations, Feature extraction, Generators,
Reliability, Task analysis
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Roy, P.R.[Pankaj Raj],
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Seoud, L.[Lama],
Predicting Next Local Appearance for Video Anomaly Detection,
MVA21(1-5)
DOI Link
2109
Training, Benchmark testing, Anomaly detection, Videos
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Zhang, C.,
Li, G.,
Su, L.,
Zhang, W.,
Huang, Q.,
Video Anomaly Detection Using Open Data Filter and Domain Adaptation,
VCIP20(395-398)
IEEE DOI
2102
Training, Training data, Anomaly detection, Feature extraction,
Data models, Testing, Adaptation models, anomaly detection, domain adaptation
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Wei, H.[Hao],
Li, K.[Kai],
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CVS19(330-342).
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1912
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Davy, A.,
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Morel, J.M.[Jean-Michel],
Detection of Small Anomalies on Moving Background,
ICIP19(2015-2019)
IEEE DOI
1910
Anomaly Detection, Optical Flow
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Bao, T.L.[Tian-Long],
Ding, C.H.[Chun-Hui],
Karmoshi, S.[Saleem],
Zhu, M.[Ming],
Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders,
ISVC16(II: 83-91).
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1701
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Simultaneous sparsity model for multi-perspective video anomaly
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ICIP14(2314-2318)
IEEE DOI
1502
Encoding
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Xu, D.[Dan],
Wu, X.Y.[Xin-Yu],
Song, D.Z.[De-Zhen],
Li, N.N.[Nan-Nan],
Chen, Y.L.[Yen-Lun],
Hierarchical activity discovery within spatio-temporal context for
video anomaly detection,
ICIP13(3597-3601)
IEEE DOI
1402
Visual surveillance
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Saligrama, V.[Venkatesh],
Chen, Z.[Zhu],
Video anomaly detection based on local statistical aggregates,
CVPR12(2112-2119).
IEEE DOI
1208
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Interactive Motion Segmentation .