Papers with Code 아카이브

Video Anomaly Detection

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AmmarKamoona/Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding

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Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding

https://github.com/Darcyddx/Video-LLM

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Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight

https://github.com/Hoangpham13579/end_to_end_anomaly_detection

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Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System

https://github.com/LiUzHiAn/hf2vad

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A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction

https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly

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When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos

https://github.com/MoonBlvd/tad-IROS2019

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Unsupervised Traffic Accident Detection in First-Person Videos

https://github.com/RomeroBarata/skeleton_based_anomaly_detection

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Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

https://github.com/SkiddieAhn/Paper-AnyAnomaly

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AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM

https://github.com/SkiddieAhn/Paper-VideoPatchCore

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VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection

https://github.com/XiaoJian923/Divide-and-Conquer

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Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach

https://github.com/XiaoJian923/Kinematics-VAD

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Human Kinematics-inspired Skeleton-based Video Anomaly Detection

https://github.com/YBQian-Roy/UCF-Crime-DVS

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks

https://github.com/Yuchen413/AnomalyRuler

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Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models

https://github.com/Yui010206/MoPRL

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Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

https://github.com/adelic99/seeker

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Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection

https://github.com/aimagelab/novelty-detection

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Latent Space Autoregression for Novelty Detection

https://github.com/ais-clemson/visiongpt

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VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation

https://github.com/akanuasiegbu/bipoco

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BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection

https://github.com/akanuasiegbu/leveraging-trajectory-prediction-for-pedestrian-video-anomaly-detection

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Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection

https://github.com/aleflabo/COSKAD

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Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection

https://github.com/aleflabo/MoCoDAD

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Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

https://github.com/alexandrosstergiou/TrajREC

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Holistic Representation Learning for Multitask Trajectory Anomaly Detection

https://github.com/allenyljiang/anomaly-detection-in-sequences

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Reasonable Anomaly Detection in Long Sequences

https://github.com/amirmk89/gepc

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Graph Embedded Pose Clustering for Anomaly Detection

https://github.com/anasemad11/c2fpl

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A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection

https://github.com/anasemad11/clap

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Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline

https://github.com/anilosmantur/conditioned_video_anomaly_diffusion

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Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations

https://github.com/arielzc/de-net

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Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection

https://github.com/aseuteurideu/LearningNotToReconstructAnomalies

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Learning Not to Reconstruct Anomalies

https://github.com/aseuteurideu/STEAL

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Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection

https://github.com/benedictstar/Joint-VAD

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Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection

https://github.com/boschresearch/LatentOE-AD

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Latent Outlier Exposure for Anomaly Detection with Contaminated Data

https://github.com/carolchenyx/mgfn

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MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection

https://github.com/codnjsqkr/FastAno_official

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FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation

https://github.com/cool-xuan/bn-wvad

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BatchNorm-based Weakly Supervised Video Anomaly Detection

https://github.com/demetrislappas/ddl

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Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.