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Video Anomaly Detection on ShanghaiTech

0.867Micro AUC

MULDE

Updated 4d ago

Evaluation Results

Method
MULDE 2024.03	0.867	-	-	0.915	-	-	-
MULDE (beta=0) 2024.03	0.864	-	-	0.91	-	-	-
AccI-VAD 2024.03	0.859	-	-	0.896	-	-	-
STG-NF 2024.03	0.859	-	-	-	-	-	-
Jigsaw-Puzzle 2024.03	0.843	-	-	0.898	-	-	-
MSMA 2024.03	0.841	-	-	0.902	-	-	-
SSMTL++ 2024.03	0.838	-	-	0.905	-	-	-
Two-stream 2022.09	0.837	-	-	90.8	-	-	-
SSMTL + UbNormal 2024.03	0.837	-	-	0.905	-	-	-
BA-AED + SSPCAB 2024.03	0.836	-	-	0.895	-	-	-
BAF 2022.09	0.827	-	-	89.3	-	-	-
BA-AED 2024.03	0.827	-	-	0.893	-	-	-
SSMTL 2024.03	0.824	-	-	0.893	-	-	-
HSTGCNN 2022.09	0.818	-	-	-	-	-	-
MULDE 2024.03	0.813	-	-	0.859	-	-	-
iL2SH 2022.09	0.81	-	-	87.2	-	-	-
STU-Net 2022.09	0.797	-	-	87.6	-	-	-
CAC 2022.09	0.793	-	-	-	-	-	-
CAC 2024.03	0.793	-	-	-	-	-	-
CAE-SVM 2024.03	0.787	-	-	0.849	-	-	-
FPDM 2024.03	0.786	-	-	-	-	-	-
MULDE 2024.03	0.784	-	-	0.86	-	-	-
VABD 2022.09	0.782	-	-	-	-	-	-
r-GAN 2022.09	0.779	-	-	-	-	-	-
CT-D2GAN 2024.03	0.777	-	-	-	-	-	-
LLSH 2022.09	0.776	-	-	85.9	-	-	-
MSMA 2024.03	0.767	-	-	0.842	-	-	-
ROADMAP 2022.09	0.766	-	-	-	-	-	-
BMAN 2022.09	0.762	-	-	-	-	-	-
HF2-VAD 2022.09	0.762	-	-	-	-	-	-
AccI-VAD_GMM 2024.03	0.762	-	-	0.829	-	-	-
HF2 2024.03	0.762	-	-	-	-	-	-
GEPC 2022.09	0.761	-	-	-	-	-	-
MTP 2022.09	0.76	-	-	-	-	-	-
VEC-AM 2022.09	0.748	-	-	-	-	-	-
VEC 2024.03	0.748	-	-	-	-	-	-
Scene-Aware 2024.03	0.747	-	-	-	-	-	-
SSAGAN 2022.09	0.743	-	-	-	-	-	-
MPN 2022.09	0.738	-	-	-	-	-	-
SmithNet 2022.09	0.738	-	-	-	-	-	-
AMMC-Net 2022.09	0.737	-	-	-	-	-	-
Multispace 2022.09	0.736	-	-	-	-	-	-
MPED-RNN 2022.09	0.734	-	-	-	-	-	-
MESDnet 2022.09	0.732	-	-	-	-	-	-
F2PN 2022.09	0.73	-	-	-	-	-	-
FFP 2022.09	0.728	-	-	-	-	-	-
AccI-VAD_KNN 2024.03	0.719	-	-	0.831	-	-	-
MemAE 2022.09	0.712	-	-	-	-	-	-
MNAD 2022.09	0.705	-	-	-	-	-	-
Ada-Net 2022.09	0.7	-	-	-	-	-	-
SRNN-AE 2022.09	0.696	-	-	-	-	-	-
Conv-AE 2019.03	-	0.704	-	-	-	-	-
TSC SRNN 2019.03	-	0.68	-	-	-	-	-
Liu et al. 2019.03	-	0.728	-	-	-	-	-
MPED-RNN 2019.03	-	0.734	-	-	-	-	-
Conv-AE 2019.08	-	0.704	-	-	-	-	-
TSC-rRNN 2019.08	-	0.68	-	-	-	-	-
Liu et al. 2019.08	-	0.728	-	-	-	-	-
Abati et al. 2019.08	-	0.725	-	-	-	-	-
Morais et al. 2019.08	-	0.734	-	-	-	-	-
Multi-timescale model (Ours) 2019.08	-	0.7603	-	-	-	-	-
CAE 2020.08	-	0.609	-	-	-	-	-
SRNN 2020.08	-	0.68	-	-	-	-	-
PDE-AE 2020.08	-	0.725	-	-	-	-	-
Mem-AE 2020.08	-	0.712	-	-	-	-	-
Frame-Prediction 2020.08	-	0.728	-	-	-	-	-
Skeleton-Trajectories + MPED-RNN 2020.08	-	0.734	-	-	-	-	-
AnoPCN 2020.08	-	0.736	-	-	-	-	-
Prediction&Reconstruction 2020.08	-	0.73	-	-	-	-	-
VEC-A 2020.08	-	0.747	-	-	-	-	-
VEC-AM 2020.08	-	0.748	-	-	-	-	-
Zhong et al. 2020.07	-	0.7644	-	-	-	-	-
Zaheer et al. [53] 2020.07	-	0.8416	-	-	-	-	-
Zaheer et al. [21] 2020.07	-	0.8967	-	-	-	-	-
Proposed (our network + our loss) 2020.07	-	0.8742	-	-	-	-	-
MemAE 2022.04	-	-	0.712	-	-	-	-
MNAD-R 2022.04	-	-	0.698	-	-	-	-
Frame-Pred. 2022.04	-	-	0.728	-	-	-	-
MNAD-P 2022.04	-	-	0.705	-	-	-	-
VEC 2022.04	-	-	0.748	-	-	-	-
Stacked RNN 2022.04	-	-	0.68	-	-	-	-
AnoPCN 2022.04	-	-	0.736	-	-	-	-
AMMC-Net 2022.04	-	-	0.737	-	-	-	-
HF2-VAD 2022.04	-	-	0.762	-	-	-	-
AMSRC 2022.04	-	-	0.763	-	-	-	-
OADA 2022.09	-	-	-	84.9	-	-	-
SSMTL 2022.09	-	-	-	90.2	-	-	-
RTFM 2022.10	-	0.9721	-	-	-	-	-
[17] 2022.10	-	0.853	-	-	-	-	-
Mist 2022.10	-	0.9483	-	-	-	-	-
[28] 2022.10	-	0.9748	-	-	-	-	-
[14] 2022.10	-	0.9732	-	-	-	-	-
MIL-BERT 2022.10	-	0.9754	-	-	-	-	-
MemAE 2022.03	-	0.712	-	-	-	-	-
MNAD 2022.03	-	0.725	-	-	-	-	-
SSMT 2022.03	-	0.835	-	-	-	-	-
SSMT 2022.03	-	0.902	-	-	-	-	-
dummyAE 2022.03	-	0.787	-	-	20.7	44.5	-
SSMT 2022.03	-	0.893	-	-	-	-	-
BAF 2022.03	-	0.827	-	-	41.43	78.79	-

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