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Anomaly Detection 벤치마크

Anomaly Detection on ShanghaiTech

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AUC

61.28 70.51 79.74 88.97 98.2 2017-10 2026-09 Sparse Coding Stacked RNN — 68.0 (2017-10-01) STAN — 76.2 (2018-04-23) Object-centric AE — 78.7 (2018-12-11) MPED-RNN — 73.4 (2019-03-08) Multi-timescale Prediction — 76.03 (2019-08-12) Any-Shot Sequential — 71.6 (2020-04-05) Background-Agnostic Framework — 82.7 (2020-08-27) SSMTL — 82.4 (2020-11-15) SSMTL+UBnormal — 83.7 (2021-11-16) Background- Agnostic Framework+SSPCAB — 83.6 (2021-11-17) MoPRL — 83.35 (2021-12-07) ASTNet — 73.6 (2022-05-25) SSMTL++v2 — 83.8 (2022-07-16) SSMTL++v1 — 82.9 (2022-07-16) Jigsaw-VAD — 84.3 (2022-07-20) two-stream — 83.7 (2022-09-07) SSMTL+++SSMCTB — 83.6 (2022-09-25) STPT — 77.1 (2022-10-27) STG-NF — 85.9 (2022-11-20) AI-VAD — 85.94 (2022-12-01) EVAL — 76.63 (2022-12-15) DMAD — 78.8 (2023-03-09) DAC(STG-NF + Jigsaw) — 87.72 (2023-09-26) MAMA — 76.5 (2024-02-26) MULDE-object-centric-micro — 86.7 (2024-03-21) MULDE-frame-centric-micro — 81.3 (2024-03-21) TSGAD — 80.6 (2024-04-29) PGM — 61.28 (2024-07-08) AnomalyRuler — 85.2 (2024-07-14) VideoPatchCore — 85.1 (2024-09-24) PA-VAD — 98.2 (2025-12-07) Sparse Coding Stacked RNN — 68.0 (2017-10-01) STAN — 76.2 (2018-04-23) Object-centric AE — 78.7 (2018-12-11) Background-Agnostic Framework — 82.7 (2020-08-27) SSMTL+UBnormal — 83.7 (2021-11-16) SSMTL++v2 — 83.8 (2022-07-16) Jigsaw-VAD — 84.3 (2022-07-20) STG-NF — 85.9 (2022-11-20) AI-VAD — 85.94 (2022-12-01) DAC(STG-NF + Jigsaw) — 87.72 (2023-09-26) PA-VAD — 98.2 (2025-12-07)
RankModel AUCRBDCTBDC Extra Training Data PaperCodeYear
1 PA-VAD 자동 추출 98.2 PA-VAD: Diffusion-Based Pseudo-Only Video Anomaly Detection via Domain-Aligned Memory Updates 2025
2 DAC(STG-NF + Jigsaw) 87.72% Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach XiaoJian923/Divide-and-Conquer 2023
3 MULDE-object-centric-micro 86.7% MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection 2024
4 AI-VAD 85.94% An Attribute-based Method for Video Anomaly Detection openvinotoolkit/anomalib · talreiss/Mean-Shifted-Anomaly-Detection · talreiss/accurate-interpretable-vad · +1 2022
5 STG-NF 85.9% Normalizing Flows for Human Pose Anomaly Detection orhir/stg-nf 2022
6 AnomalyRuler 85.2% Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models Yuchen413/AnomalyRuler 2024
7 VideoPatchCore 85.1% VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection SkiddieAhn/Paper-VideoPatchCore 2024
8 Jigsaw-VAD 84.3% Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles gdwang08/jigsaw-vad 2022
9 SSMTL++v2 83.8%47.1085.60 SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection 2022
10 SSMTL+UBnormal 83.7%47.1586.15 UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection lilygeorgescu/ubnormal 2021
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