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IEEE DOI
1003
BibRef
Cheng, K.W.[Kai-Wen],
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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
Asad, M.[Mujtaba],
Yang, J.[Jie],
Tu, E.[Enmei],
Chen, L.M.[Li-Ming],
He, X.J.[Xiang-Jian],
Anomaly3D: Video anomaly detection based on 3D-normality clusters,
JVCIR(75), 2021, pp. 103047.
Elsevier DOI
2103
Spatiotemporal latent features, 3D-CAE, Anomaly detection,
Video analysis, Autonomous video surveillance
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.R.[Yi-Rui],
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
Ali, M.M.[Manal Mostafa],
Real-time video anomaly detection for smart surveillance,
IET-IPR(17), No. 5, 2023, pp. 1375-1388.
DOI Link
2304
anomaly detection, background subtraction,
deep learning, real-time, surveillance
BibRef
Kommanduri, R.[Rangachary],
Ghorai, M.[Mrinmoy],
Bi-READ: Bi-Residual AutoEncoder based feature enhancement for video
anomaly detection,
JVCIR(95), 2023, pp. 103860.
Elsevier DOI
2309
Anomaly, Residual connections, Optical flow,
Unsupervised learning, Appearance consistency, Motion consistency
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
Liu, Y.[Yang],
Yang, D.K.[Ding-Kang],
Wang, Y.[Yan],
Liu, J.[Jing],
Liu, J.[Jun],
Boukerche, A.[Azzedine],
Sun, P.[Peng],
Song, L.[Liang],
Generalized Video Anomaly Event Detection: Systematic Taxonomy and
Comparison of Deep Models,
Surveys(56), No. 7, April 2024, pp. xx-yy.
DOI Link
2405
Anomaly detection, video understanding, deep learning, intelligent
survillance system
BibRef
Sun, Q.Y.[Qi-Yue],
Yang, Y.[Yang],
Xu, H.X.[Hao-Xuan],
Li, Z.[Zezhou],
Liu, Y.X.[Yun-Xia],
Wang, H.J.[Hong-Jun],
MG-KG: Unsupervised video anomaly detection based on motion guidance
and knowledge graph,
IVC(162), 2025, pp. 105644.
Elsevier DOI
2510
Anomaly detection, Video understanding, Motion guidance, Knowledge graph
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
Qiu, S.M.[Shao-Ming],
Ye, J.F.[Jing-Feng],
Zhao, J.C.[Jian-Cheng],
He, L.[Lei],
Liu, L.Y.[Liang-Yu],
E, B.C.[Bi-Cong],
Huang, X.C.[Xin-Chen],
Video anomaly detection guided by clustering learning,
PR(153), 2024, pp. 110550.
Elsevier DOI
2405
Video anomaly detection,
Spatial-temporal cascade auto-encoder, Clustering learning, Memory-guided
BibRef
Sun, Z.[Zhe],
Wang, P.P.[Pan-Pan],
Zheng, W.[Wang],
Zhang, M.[Meng],
Dual GroupGAN: An unsupervised four-competitor (2V2) approach for
video anomaly detection,
PR(153), 2024, pp. 110500.
Elsevier DOI
2405
Video anomaly detection, Dual GroupGAN, SE-U-Net, SE-VAE, weighting strategy
BibRef
Li, S.F.[Shi-Feng],
Cheng, Y.[Yan],
Zhang, L.[Liang],
Luo, X.[Xi],
Zhang, R.X.[Rui-Xuan],
Video anomaly detection based on a multi-layer reconstruction
autoencoder with a variance attention strategy,
IVC(146), 2024, pp. 105011.
Elsevier DOI Code:
WWW Link.
2405
Video anomaly detection, Motion loss function,
Variance attention strategy, Multi-layer reconstruction
BibRef
Tao, Y.R.[Yi-Ran],
Hu, Y.[Yaosi],
Chen, Z.Z.[Zhen-Zhong],
Memory-guided representation matching for unsupervised video anomaly
detection,
JVCIR(101), 2024, pp. 104185.
Elsevier DOI
2406
Video anomaly detection, Video understanding, Representation learning
BibRef
Krishnan, S.R.[Sreedevi R.],
Amudha, P.,
Sivakumari, S.,
Comprehensive survey on video anomaly detection using deep learning
techniques,
IJCVR(14), No. 4, 2024, pp. 445-466.
DOI Link
2407
BibRef
Fan, Y.[Yidan],
Yu, Y.X.[Yong-Xin],
Lu, W.H.[Wen-Huan],
Han, Y.[Yahong],
Weakly-Supervised Video Anomaly Detection With Snippet Anomalous
Attention,
CirSysVideo(34), No. 7, July 2024, pp. 5480-5492.
IEEE DOI
2407
Feature extraction, Anomaly detection, Task analysis,
Location awareness, Training, Optimization, Annotations,
multi-branch supervision
BibRef
Zeng, X.L.[Xian-Lin],
Jiang, Y.[Yalong],
Wang, Y.F.[Yu-Feng],
Fu, Q.[Qiang],
Ding, W.R.[Wen-Rui],
Progressive prediction: Video anomaly detection via multi-grained
prediction,
IET-IPR(18), No. 10, 2024, pp. 2568-2583.
DOI Link
2408
unsupervised learning, video signal processing, video surveillance
BibRef
Paulraj, S.[Shalmiya],
Vairavasundaram, S.[Subramaniyaswamy],
M2VAD: Multiview multimodality transformer-based weakly supervised
video anomaly detection,
IVC(149), 2024, pp. 105139.
Elsevier DOI Code:
WWW Link.
2408
Intelligent video surveillance, Multiview, Multimodality,
Space-time transformer, SpectFormer
BibRef
Su, Y.[Yong],
Tan, Y.[Yuyu],
An, S.[Simin],
Xing, M.[Meng],
Feng, Z.Y.[Zhi-Yong],
Semantic-driven dual consistency learning for weakly supervised video
anomaly detection,
PR(157), 2025, pp. 110898.
Elsevier DOI
2409
Video anomaly detection, Weakly-supervised, Dual consistency, Cross-modal
BibRef
Biradar, K.M.[Kuldeep Marotirao],
Mandal, M.[Murari],
Dube, S.[Sachin],
Vipparthi, S.K.[Santosh Kumar],
Tyagi, D.K.[Dinesh Kumar],
Triplet-set feature proximity learning for video anomaly detection,
IVC(150), 2024, pp. 105205.
Elsevier DOI
2409
Anomaly detection, Triplet loss, Proximity learning,
Video surveillance, Deep learning
BibRef
Fan, J.[Jin],
Ji, Y.X.[Yu-Xiang],
Wu, H.F.[Hui-Feng],
Ge, Y.[Yan],
Sun, D.F.[Dan-Feng],
Wu, J.[Jia],
An unsupervised video anomaly detection method via Optical Flow
decomposition and Spatio-Temporal feature learning,
PRL(185), 2024, pp. 239-246.
Elsevier DOI
2410
Video anomaly detection, Optical flow, Spatiotemporal learning,
Memory network, Feature fusion
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
Xu, C.T.[Chen-Ting],
Xu, K.[Ke],
Jiang, X.H.[Xing-Hao],
Sun, T.F.[Tan-Feng],
PLOVAD: Prompting Vision-Language Models for Open Vocabulary Video
Anomaly Detection,
CirSysVideo(35), No. 6, June 2025, pp. 5925-5938.
IEEE DOI
2506
Vocabulary, Tuning, Training, Visualization, Anomaly detection,
Labeling, Data models, Annotations,
prompt tuning
BibRef
Yang, Q.Y.[Qing-Yang],
Wang, C.X.[Chuan-Xu],
Liu, P.[Peng],
Jiang, Z.[Zitai],
Li, J.J.[Jia-Jiong],
Video Anomaly Detection via self-supervised and spatio-temporal proxy
tasks learning,
PR(158), 2025, pp. 111021.
Elsevier DOI Code:
WWW Link.
2411
Video Anomaly Detection, Self-supervised learning model,
Spatio-temporal decoupling, Proxy tasks
BibRef
Zanella, L.[Luca],
Liberatori, B.[Benedetta],
Menapace, W.[Willi],
Poiesi, F.[Fabio],
Wang, Y.M.[Yi-Ming],
Ricci, E.[Elisa],
Delving into CLIP latent space for Video Anomaly Recognition,
CVIU(249), 2024, pp. 104163.
Elsevier DOI Code:
WWW Link.
2412
Video anomaly detection and recognition, Multi-modal learning
BibRef
Cao, C.Q.[Cong-Qi],
Zhang, H.[Hanwen],
Lu, Y.[Yue],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
Scene-Dependent Prediction in Latent Space for Video Anomaly
Detection and Anticipation,
PAMI(47), No. 1, January 2025, pp. 224-239.
IEEE DOI
2412
Feature extraction, Anomaly detection, Diffusion models,
Predictive models, Decoding, Training, Surveillance,
diffusion models
BibRef
Anjali, S.,
Don, S.,
Generalised video anomaly detection: a systematic review,
IJCVR(15), No. 4, 2025, pp. 431-458.
DOI Link
2507
Survey, Video Anomaly.
BibRef
Chen, C.L.[Cheng-Lizhao],
Liu, X.Y.[Xin-Yu],
Song, M.K.[Meng-Ke],
Li, L.[Luming],
Yuan, S.J.[Shao-Jiang],
Yu, X.[Xu],
Pang, S.[Shanchen],
Unveiling Context-Related Anomalies: Knowledge Graph Empowered
Decoupling of Scene and Action for Human-Related Video Anomaly
Detection,
CirSysVideo(35), No. 8, August 2025, pp. 8071-8085.
IEEE DOI Code:
WWW Link.
2508
Anomaly detection, Feature extraction, Accuracy, Visualization,
Knowledge graphs, Training, Circuits and systems, Uncertainty,
deep learning
BibRef
Pitchandi, P.[Perumal],
Sadu, V.B.[Vijaya Bhaskar],
Kalaipoonguzhali, V.,
Arivukarasi, M.,
A novel video anomaly detection using hybrid sand cat Swarm
optimization with backpropagation neural network by UCSD Ped 1
dataset,
JVCIR(108), 2025, pp. 104414.
Elsevier DOI
2503
Anomaly detection, Sand cat swarm optimization,
Backpropagation neural algorithm, Improved service quality,
Improved economic efficiency
BibRef
Han, X.S.[Xing-Shuo],
Wang, X.[Xiao],
Liu, W.[Wei],
Ye, L.P.[Li-Ping],
Xu, X.[Xin],
Retrieving and Reasoning: Multivariate Feature and Attribute
Cooperation for Video Anomaly Detection,
SPLetters(32), 2025, pp. 1595-1599.
IEEE DOI
2505
Feature extraction, Vectors, Semantics, Optical flow, Cognition,
Databases, Training, Neural networks, Association rule learning,
vector retrieval database
BibRef
Yang, Z.[Zhen],
Wang, G.D.[Guo-Dong],
Guo, Y.F.[Yuan-Fang],
Bao, X.[Xiuguo],
Huang, D.[Di],
Anomaly-aware self-supervised feature learning for weakly supervised
video anomaly detection,
CVIU(257), 2025, pp. 104379.
Elsevier DOI
2505
Video anomaly detection, Weakly supervised learning,
Self-supervised representation learning
BibRef
Kim, J.[Jinmyeong],
Cho, S.B.[Sung-Bae],
Unsupervised video anomaly detection by memory network with
autoencoders in euclidean and non-euclidean spaces,
PR(167), 2025, pp. 111759.
Elsevier DOI
2506
Video anomaly detection, Autoencoder, Non-euclidean space,
Constant curvature manifold
BibRef
Liu, J.[Jing],
Liu, Y.[Yang],
Lin, J.[Jieyu],
Li, J.L.[Jie-Lin],
Cao, L.[Liang],
Sun, P.[Peng],
Hu, B.[Bo],
Song, L.[Liang],
Boukerche, A.[Azzedine],
Leung, V.C.M.[Victor C.M.],
Networking Systems for Video Anomaly Detection: A Tutorial and Survey,
Surveys(57), No. 10, May 2025, pp. xx-yy.
DOI Link
2507
Survey, Anomaly Detection. Video anomaly detection, intelligent surveillance,
representation learning, normality learning
BibRef
Kim, Y.J.[Yu-Jun],
Kim, Y.G.[Young-Gab],
MPE: Multi-frame prediction error-based video anomaly detection
framework for robust anomaly inference,
PR(164), 2025, pp. 111595.
Elsevier DOI
2504
Multi-frame prediction error, Anomaly detection,
Convolutional neural networks, Video surveillance
BibRef
Huang, J.[Jia],
Quan, W.[Wei],
Li, X.[Xiwen],
Visual anomaly detection algorithms: Development and Frontier review,
JVCIR(112), 2025, pp. 104585.
Elsevier DOI
2511
Visual anomaly detection, Algorithm taxonomy,
Benchmark datasets, Evaluation metrics
BibRef
Wang, X.[Xuxu],
Wu, X.Y.[Xiao-Yu],
Liu, Z.[Zihao],
Enhancing Video Anomaly Understanding via Multi-Task Instruction
Tuning,
SPLetters(32), 2025, pp. 4359-4363.
IEEE DOI
2512
Videos, Multitasking, Training, Annotations, Accuracy, Pipelines,
Question generation,
Video anomaly detection multi-modal learning video anomaly understanding
BibRef
Luo, Z.M.[Zhi-Ming],
Huang, S.[Shuheng],
Yang, K.[Kun],
Gao, J.Z.[Jian-Zhe],
Li, S.[Shaozi],
FADMB: Fully attention-based dual memory bank network for weakly
supervised video anomaly detection,
PR(172), 2026, pp. 112288.
Elsevier DOI
2512
Video anomaly detection, Weakly supervised, Attention,
Top- selection mechanism, Memory bank
BibRef
Hu, Y.[Yongting],
Zhong, Y.H.[Yuan-Hong],
Li, J.[Jinkai],
Wang, X.[Xin],
Learning multiscale residual prototypes and global-local
correspondence for video anomaly detection,
CVIU(262), 2025, pp. 104524.
Elsevier DOI
2512
Video anomaly detection, Memory network, Multiscale residual,
Global-local correspondence
BibRef
Li, Q.[Qun],
Gu, P.[Peng],
Gao, X.P.[Xin-Ping],
Bhanu, B.[Bir],
TSMnet: Two-step separation pipeline based on threshold shrinkage
memory network for weakly-supervised video anomaly detection,
PRL(199), 2026, pp. 13-20.
Elsevier DOI
2512
Weakly supervised video anomaly detection,
Spatio-temporal adapter, Memory network, Contrastive learning
BibRef
Chen, P.Z.[Peng-Zhan],
Du, S.D.[Sheng-Dong],
Zhao, X.L.[Xiao-Le],
Hu, J.[Jie],
Li, J.J.[Jing-Jing],
Li, T.R.[Tian-Rui],
DCTFormer: A Dual-Branch Transformer With Cloze Tests for Video
Anomaly Detection,
MultMed(27), 2025, pp. 9022-9032.
IEEE DOI
2601
Videos, Anomaly detection, Transformers, Optical flow, Autoencoders,
Dynamics, Training, Feature extraction, Correlation, Semantics,
unsupervised learning
BibRef
Luo, W.[Wei],
Xing, P.[Peng],
Cao, Y.[Yunkang],
Yao, H.M.[Hai-Ming],
Shen, W.M.[Wei-Ming],
Li, Z.C.[Ze-Chao],
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration
Attention Network for Unsupervised Anomaly Detection,
CirSysVideo(36), No. 2, February 2026, pp. 2464-2477.
IEEE DOI
2602
Image restoration, Image reconstruction, Feature extraction,
Anomaly detection, Semantics, Training, Random access memory,
uncertainty-integrated anomaly perception
BibRef
Nayak, R.[Rashmiranjan],
Pati, U.C.[Umesh Chandra],
Das, S.K.[Santos Kumar],
MGLA-DSNet: Multi-head global-local attention-enabled dual-stream
network for weakly supervised video anomaly detection,
JVCIR(116), 2026, pp. 104744.
Elsevier DOI
2603
Deep learning, MGLA-DSNet, Multiple instance learning,
Multi-head global-local attention, Spatiotemporal features,
Weakly supervised video anomaly detection
BibRef
Deng, J.X.[Ji-Xiang],
Liu, Y.[Ying],
Li, C.G.[Chun-Guang],
Extended Graph Learning for Weakly Supervised Video Anomaly Detection,
CirSysVideo(36), No. 3, March 2026, pp. 3117-3130.
IEEE DOI
2603
Videos, Anomaly detection, Training data, Training,
Graph convolutional networks, Annotations, Circuits and systems,
weakly supervised learning
BibRef
Li, Z.[Zhaoyi],
Shi, C.R.[Chen-Rui],
Sun, C.[Che],
Wu, Y.W.[Yu-Wei],
Robust video anomaly detection via causal feature-guided data
augmentation,
CVIU(265), 2026, pp. 104671.
Elsevier DOI
2603
Video anomaly detection, Data augmentation,
Causal generative model, Robustness analysis
BibRef
He, P.[Ping],
Gao, X.N.[Xiao-Nan],
Li, H.B.[Hui-Bin],
MG-TVMF: Multi-grained text-video matching and fusing for weakly
supervised video anomaly detection,
PR(176), 2026, pp. 113201.
Elsevier DOI
2603
Weakly-supervised anomaly detection, Multi-grained text-video matching,
Optimal transport
BibRef
Wang, C.X.[Chuan-Xu],
Jiang, Z.[Zitai],
Deng, H.G.[Hai-Gang],
Yan, C.J.[Chun-Juan],
A video anomaly detection framework based on semantic consistency and
multi-attribute feature complementarity,
PR(170), 2026, pp. 112016.
Elsevier DOI Code:
WWW Link.
2509
Multi-attribute, Video anomaly detection, Semantic consistency,
Spatial-channel feature complementarity
BibRef
Lyu, J.H.[Jia-Hao],
Zhao, M.H.[Ming-Hua],
Hu, J.[Jing],
Xi, R.T.[Run-Tao],
Huang, X.W.[Xue-Wen],
Du, S.L.[Shuang-Li],
Shi, C.[Cheng],
Ma, T.[Tian],
Bidirectional skip-frame prediction for video anomaly detection with
intra-domain disparity-driven attention,
PR(170), 2026, pp. 112010.
Elsevier DOI Code:
WWW Link.
2509
Video anomaly detection, Bidirectional frame prediction,
Intra-domain disparity, Attention mechanism, Autoencoder
BibRef
Liu, Y.[Yishuo],
Wang, C.[Chuanxu],
Yang, Q.Y.[Qing-Yang],
Li, L.X.[Lan-Xiao],
Wang, B.H.[Bing-Hui],
Self-supervised learning video anomaly detection based on time
interval prediction and noise classification,
PR(171), 2026, pp. 112198.
Elsevier DOI
2510
Video anomaly detection, Self-supervised learning,
Fine-grained modeling, Diffusion model, Self-attention mechanism
BibRef
Leng, J.X.[Jia-Xu],
Zhang, Y.M.[Yu-Meng],
Tan, M.[Mingpi],
Kuang, C.J.[Chang-Jiang],
Wu, Z.J.[Zhan-Jie],
Gan, J.[Ji],
Gao, X.B.[Xin-Bo],
Dual-Space Normalizing Flow for Unsupervised Video Anomaly Detection,
IP(34), 2025, pp. 6432-6445.
IEEE DOI
2510
Videos, Anomaly detection, Data models, Feature extraction,
Legged locomotion, Image reconstruction, Convolution,
adaptive weighted approximate mass
BibRef
Fu, Y.[Yan],
Hou, T.[Ting],
Ye, O.[Ou],
Ye, G.L.[Gao-Lin],
A video anomaly detection and classification method based on
cross-modal feature alignment,
IVC(167), 2026, pp. 105874.
Elsevier DOI
2602
Video anomaly detection, Dual-branch architecture,
Persistent anomaly, Sudden anomaly
BibRef
Guo, C.[Chongye],
Li, L.[Li],
Ren, Y.L.[Yan-Li],
Zhang, X.P.[Xin-Peng],
Feng, G.R.[Guo-Rui],
Aligning Normal Representations in Diffusion Model for Video Anomaly
Detection,
CirSysVideo(36), No. 2, February 2026, pp. 2083-2094.
IEEE DOI
2602
Diffusion models, Anomaly detection, Videos, Feature extraction,
Semantics, Image reconstruction, Noise reduction, Training,
group-supervised learning
BibRef
Shi, C.R.[Chen-Rui],
Sun, C.[Che],
Wu, Y.W.[Yu-Wei],
Jia, Y.D.[Yun-De],
Video Anomaly Detection via Sequentially Learning Multiple Pretext
Tasks,
ICCV23(10296-10306)
IEEE DOI
2401
BibRef
Yang, Z.W.[Zhi-Wei],
Liu, J.[Jing],
Wu, Z.Y.[Zhao-Yang],
Wu, P.[Peng],
Liu, X.T.[Xiao-Tao],
Video Event Restoration Based on Keyframes for Video Anomaly
Detection,
CVPR23(14592-14601)
IEEE DOI
2309
BibRef
Li, F.[Fei],
Liu, W.X.[Wen-Xuan],
Chen, J.J.[Jing-Jing],
Zhang, R.[Ruixu],
Wang, Y.[Yuran],
Zhong, X.[Xian],
Wang, Z.[Zheng],
Anomize: Better Open Vocabulary Video Anomaly Detection,
CVPR25(29203-29212)
IEEE DOI
2508
Vocabulary, Visualization, Accuracy, Face recognition, Encoding,
Anomaly detection, Videos, open vocabulary, video anomaly detection
BibRef
Zhang, H.X.[Hua-Xin],
Xu, X.H.[Xiao-Hao],
Wang, X.[Xiang],
Zuo, J.[Jialong],
Huang, X.N.[Xiao-Nan],
Gao, C.X.[Chang-Xin],
Zhang, S.J.[Shan-Jun],
Yu, L.[Li],
Sang, N.[Nong],
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any
Granularity,
CVPR25(13843-13853)
IEEE DOI Code:
WWW Link.
2508
Visualization, Accuracy, Annotations, Manuals, Benchmark testing,
Performance gain, Anomaly detection, Standards, Videos,
multimodal large language model
BibRef
Huang, Y.Z.[Yu-Zhi],
Li, C.X.[Chen-Xin],
Zhang, H.T.[Hai-Tao],
Lin, Z.X.[Zi-Xu],
Lin, Y.L.[Yun-Long],
Liu, H.[Hengyu],
Li, W.Y.[Wu-Yang],
Liu, X.Y.[Xin-Yu],
Gao, J.[Jiechao],
Huang, Y.[Yue],
Ding, X.H.[Xing-Hao],
Yuan, Y.X.[Yi-Xuan],
Track Any Anomalous Object: A Granular Video Anomaly Detection
Pipeline,
CVPR25(8689-8699)
IEEE DOI Code:
WWW Link.
2508
Location awareness, Video tracking, Surveillance, Video sequences,
Pipelines, Transforms, Object recognition, Anomaly detection, Videos
BibRef
Ye, M.[Muchao],
Liu, W.[Weiyang],
He, P.[Pan],
VERA: Explainable Video Anomaly Detection via Verbalized Learning of
Vision-Language Models,
CVPR25(8679-8688)
IEEE DOI
2508
Training, Adaptation models, Computational modeling, Training data,
Cognition, Reflection, Anomaly detection, Tuning, Videos,
vision-language models
BibRef
Hu, H.[Han],
Du, W.L.[Wen-Li],
Liao, P.[Peng],
Wang, B.[Bing],
Fan, S.Y.[Si-Yuan],
Noise-Resistant Video Anomaly Detection via RGB Error-Guided
Multiscale Predictive Coding and Dynamic Memory,
CVPR25(19109-19119)
IEEE DOI
2508
Dynamics, Interference, Memory modules, Predictive coding,
Benchmark testing, Background noise, Anomaly detection, Videos,
memory network
BibRef
Gao, S.[Shibo],
Yang, P.P.[Pei-Pei],
Huang, L.L.[Lin-Lin],
Scene-Adaptive SVAD Based On Multi-Modal Action-Based Feature
Extraction,
ACCV24(III: 329-346).
Springer DOI
2412
Semi-Supervised Video Anomaly Detection
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.R.[Xue-Rong],
Pang, G.S.[Guan-Song],
Sun, Y.J.[Yu-Jia],
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.Z.[Bin-Zhu],
Nan, G.S.[Guo-Shun],
Zhang, J.Y.[Jia-Yang],
Xu, J.R.[Jun-Rui],
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.K.[Yuan-Kai],
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.W.[Zong-Wei],
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],
Minezawa, A.[Akira],
Memory-efficient and GPU-oriented visual anomaly detection with
incremental dimension reduction,
VAND23(1-9)
IEEE DOI
2309
BibRef
Grcic, M.[Matej],
Šaric, J.[Josip],
Šegvic, S.[Siniša],
On Advantages of Mask-level Recognition for Outlier-aware
Segmentation,
VAND23(2937-2947)
IEEE DOI
2309
BibRef
Yang, Z.[Ziyi],
Soltani, I.[Iman],
Darve, E.[Eric],
Anomaly Detection with Domain Adaptation,
VAND23(2958-2967)
IEEE DOI
2309
BibRef
Liu, Z.[Zuhao],
Wu, X.M.[Xiao-Ming],
Zheng, D.[Dian],
Lin, K.Y.[Kun-Yu],
Zheng, W.S.[Wei-Shi],
Generating Anomalies for Video Anomaly Detection with Prompt-based
Feature Mapping,
CVPR23(24500-24510)
IEEE DOI
2309
BibRef
Tien, T.D.[Tran Dinh],
Nguyen, A.T.[Anh Tuan],
Tran, N.H.[Nguyen Hoang],
Huy, T.D.[Ta Duc],
Duong, S.T.M.[Soan T.M.],
Nguyen, C.D.T.[Chanh D. Tr.],
Truong, S.Q.H.[Steven Q. H.],
Revisiting Reverse Distillation for Anomaly Detection,
CVPR23(24511-24520)
IEEE DOI
2309
BibRef
Lv, H.[Hui],
Yue, Z.Q.[Zhong-Qi],
Sun, Q.[Qianru],
Luo, B.[Bin],
Cui, Z.[Zhen],
Zhang, H.W.[Han-Wang],
Unbiased Multiple Instance Learning for Weakly Supervised Video
Anomaly Detection,
CVPR23(8022-8031)
IEEE DOI
2309
BibRef
Ouyang, Y.Q.[Yu-Qi],
Shen, G.D.[Guo-Dong],
Sanchez, V.[Victor],
Look at Adjacent Frames:
Video Anomaly Detection Without Offline Training,
RealWorld22(642-658).
Springer DOI
2304
BibRef
Wang, Y.L.[Yun-Long],
Chen, M.Y.[Ming-Yi],
Li, J.X.[Jia-Xin],
Li, H.J.[Hong-Jun],
Spatio-Temporal United Memory for Video Anomaly Detection,
SSSPR22(84-93).
Springer DOI
2301
BibRef
Sun, X.H.[Xiao-Hu],
Chen, J.Y.[Jin-Yi],
Shen, X.[Xulin],
Li, H.J.[Hong-Jun],
Transformer with Spatio-Temporal Representation for Video Anomaly
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SSSPR22(213-222).
Springer DOI
2301
BibRef
Baradaran, M.[Mohammad],
Bergevin, R.[Robert],
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],
Fuh, C.S.[Chiou-Shann],
Liu, T.L.[Tyng-Luh],
Self-supervised Sparse Representation for Video Anomaly Detection,
ECCV22(XIII:729-745).
Springer DOI
2211
BibRef
Wang, G.D.[Guo-Dong],
Wang, Y.H.[Yun-Hong],
Qin, J.[Jie],
Zhang, D.M.[Dong-Ming],
Bao, X.[Xiuguo],
Huang, D.[Di],
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw
Puzzles,
ECCV22(X:494-511).
Springer DOI
2211
BibRef
Zhu, Y.S.[Yuan-Sheng],
Bao, W.T.[Wen-Tao],
Yu, Q.[Qi],
Towards Open Set Video Anomaly Detection,
ECCV22(XXXIV:395-412).
Springer DOI
2211
BibRef
Liu, Z.[Zhian],
Nie, Y.W.[Yong-Wei],
Long, C.J.[Cheng-Jiang],
Zhang, Q.[Qing],
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
Detection,
CVPR21(14004-14013)
IEEE DOI
2111
Annotations, Feature extraction, Generators,
Reliability, Task analysis
BibRef
Roy, P.R.[Pankaj Raj],
Bilodeau, G.A.[Guillaume-Alexandre],
Seoud, L.[Lama],
Predicting Next Local Appearance for Video Anomaly Detection,
MVA21(1-5)
DOI Link
2109
Training, Benchmark testing, Anomaly detection, Videos
BibRef
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
BibRef
Sun, L.,
Chen, Y.,
Luo, W.,
Wu, H.,
Zhang, C.,
Discriminative Clip Mining for Video Anomaly Detection,
ICIP20(2121-2125)
IEEE DOI
2011
Anomaly detection, Feature extraction, Testing, Training,
Task analysis, Indexes, Surveillance, anomaly detection, contrastive pattern
BibRef
Wei, H.[Hao],
Li, K.[Kai],
Li, H.[Haichang],
Lyu, Y.F.[Yi-Fan],
Hu, X.H.[Xiao-Hui],
Detecting Video Anomaly with a Stacked Convolutional LSTM Framework,
CVS19(330-342).
Springer DOI
1912
BibRef
Davy, A.,
Desolneux, A.[Agnes],
Morel, J.M.[Jean-Michel],
Detection of Small Anomalies on Moving Background,
ICIP19(2015-2019)
IEEE DOI
1910
Anomaly Detection, Optical Flow
BibRef
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).
Springer DOI
1701
BibRef
Mo, X.[Xuan],
Monga, V.[Vishal],
Bala, R.[Raja],
Simultaneous sparsity model for multi-perspective video anomaly
detection,
ICIP14(2314-2318)
IEEE DOI
1502
Encoding
BibRef
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
BibRef
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 .