Video-Anomaly-Detection
Awesome Video Anomaly Detection
https://github.com/vt-le/Video-Anomaly-Detection
Last synced: 2 days ago
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Related works
- Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning - CVPR |  | - | [Model]() |
- Video Anomaly Detection and Explanation via Large Language Models - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- SUVAD: Semantic Understanding Based Video Anomaly Detection Using MLLM - ArXiv |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks - SNCS |  | - | [Model]() |
- Anomaly detection in surveillance videos using deep autoencoder - IJIT |  | - | [Model]() |
- A deep learning-assisted visual attention mechanism for anomaly detection in videos - MTnA |  | - | [Model]() |
- Deep learning based anomaly detection in real‑time video - MTnA |  | - | [Model]() |
- Video anomaly detection based on multi-scale optical flow spatio-temporal enhancement and normality mining - MLC |  | - | [Model]() |
- MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model - PR |  | - | [Model]() |
- Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models - ECCV |  | [](https://github.com/Yuchen413/AnomalyRuler) | [Model]() |
- Video anomaly detection with both normal and anomaly memory modules - TVC |  | [](https://github.com/SVIL2024/Pseudo-Anomaly-MemAE) | [Model]() |
- Generate anomalies from normal: a partial pseudo-anomaly augmented approach for video anomaly detection - TVC |  | [](https://github.com/OctCjy/GenerateAnomaliesFromNormal) | [Model]() |
- A novel spatio-temporal memory network for video anomaly detection - MTnA |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction - MS |  | - | [Model]() |
- FDC-Net: foreground dynamic capture with deep feature enhancement for video anomaly detection - MS |  | - | [Model]() |
- Learning dual updatable memory modules for video anomaly detection - MS |  | - | [Model]() |
- Dual-Stage attention mechanism for robust video anomaly detection and localization - SIVP |  | - | [Model]() |
- Human pose feature enhancement for human anomaly detection and tracking - IT |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks - SNCS |  | - | [Model]() |
- Anomaly detection in surveillance videos using deep autoencoder - IJIT |  | - | [Model]() |
- A deep learning-assisted visual attention mechanism for anomaly detection in videos - MTnA |  | - | [Model]() |
- Deep learning based anomaly detection in real‑time video - MTnA |  | - | [Model]() |
- MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model - PR |  | - | [Model]() |
- Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models - ECCV |  | [](https://github.com/Yuchen413/AnomalyRuler) | [Model]() |
- Video anomaly detection with both normal and anomaly memory modules - TVC |  | [](https://github.com/SVIL2024/Pseudo-Anomaly-MemAE) | [Model]() |
- Video anomaly detection based on multi-scale optical flow spatio-temporal enhancement and normality mining - MLC |  | - | [Model]() |
- Generate anomalies from normal: a partial pseudo-anomaly augmented approach for video anomaly detection - TVC |  | [](https://github.com/OctCjy/GenerateAnomaliesFromNormal) | [Model]() |
- A novel spatio-temporal memory network for video anomaly detection - MTnA |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction - MS |  | - | [Model]() |
- FDC-Net: foreground dynamic capture with deep feature enhancement for video anomaly detection - MS |  | - | [Model]() |
- Learning dual updatable memory modules for video anomaly detection - MS |  | - | [Model]() |
- Human pose feature enhancement for human anomaly detection and tracking - IT |  | - | [Model]() |
- Dual-Stage attention mechanism for robust video anomaly detection and localization - SIVP |  | - | [Model]() |
- Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks - SNCS |  | - | [Model]() |
- Anomaly detection in surveillance videos using deep autoencoder - IJIT |  | - | [Model]() |
- A deep learning-assisted visual attention mechanism for anomaly detection in videos - MTnA |  | - | [Model]() |
- Deep learning based anomaly detection in real‑time video - MTnA |  | - | [Model]() |
- MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model - PR |  | - | [Model]() |
- Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models - ECCV |  | [](https://github.com/Yuchen413/AnomalyRuler) | [Model]() |
- Video anomaly detection with both normal and anomaly memory modules - TVC |  | [](https://github.com/SVIL2024/Pseudo-Anomaly-MemAE) | [Model]() |
- Video anomaly detection based on multi-scale optical flow spatio-temporal enhancement and normality mining - MLC |  | - | [Model]() |
- Generate anomalies from normal: a partial pseudo-anomaly augmented approach for video anomaly detection - TVC |  | [](https://github.com/OctCjy/GenerateAnomaliesFromNormal) | [Model]() |
- A novel spatio-temporal memory network for video anomaly detection - MTnA |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction - MS |  | - | [Model]() |
- FDC-Net: foreground dynamic capture with deep feature enhancement for video anomaly detection - MS |  | - | [Model]() |
- Learning dual updatable memory modules for video anomaly detection - MS |  | - | [Model]() |
- Dual-Stage attention mechanism for robust video anomaly detection and localization - SIVP |  | - | [Model]() |
- Human pose feature enhancement for human anomaly detection and tracking - IT |  | - | [Model]() |
- Learning a multi-cluster memory prototype for unsupervised video anomaly detection - IS |  | [](https://github.com/WuIkun5658/MCMP) | [Model]() |
- Deep video anomaly detection in automated laboratory setting - ESWA |  | - | [Model]() |
- Adversarial diffusion for few-shot scene adaptive video anomaly detection - N |  | - | [Model]() |
- Spatio-temporal graph-based self-labeling for video anomaly detection - N |  | - | [Model]() |
- SSIM over MSE: A new perspective for video anomaly detection - NN |  | [](https://github.com/yqytomorrow/spatio-temporal-tasks) | [Model]() |
- Rethinking prediction-based video anomaly detection from local–global normality perspective - ESWA |  | [](https://github.com/Myzhao1999/LGN-Net) | [Model]() |
- AnomLite: Efficient binary and multiclass video anomaly detection - RiE |  | [](https://github.com/AnnaZverev/UCF_Crime) | [Model]() |
- Audio-Visual Collaborative Learning for Weakly Supervised Video Anomaly Detection - CVPR |  | - | [Model]() |
- Language-guided Open-world Video Anomaly Detection - | [Model]() |
- A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction - MS |  | - | [Model]() |
- ASTNet: Attention-based Residual Autoencoder for Video Anomaly Detection
- Ada-VAD: Domain Adaptable Video Anomaly Detection - SIAM |  | [](https://github.com/donglgcn/ADA-VAD) | [Model](https://drive.google.com/file/d/1LX_mihqutn4iq57Liy9LfJ5Z6GE7eWi_/view?usp=sharing) |
- Dual GroupGAN: An unsupervised four-competitor (2V2) approach for video anomaly detection - PR |  | - | [Model]() |
- Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model - ITIP |  | - | [Model](https://drive.google.com/file/d/13cmEK95NiNTSlAidcSCRdLSQnjNM2Mtk/view?usp=sharing) |
- Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline - CVPR |  | [](https://github.com/AnasEmad11/CLAP) | [Model]() |
- Context-aware Video Anomaly Detection in Long-Term Datasets - CVPR |  | - | [Model](https://drive.google.com/file/d/1IxxlDvSo8IyQHbjesRthtgLaRjeQYVqK/view?usp=sharing) |
- Harnessing Large Language Models for Training-free Video Anomaly Detection - CVPR |  | [](https://lucazanella.github.io/lavad/) | [Model]() |
- Attention-guided generator with dual discriminator GAN for real-time video anomaly detection - EPAI |  | [](https://github.com/Rituraj-ksi/A2D-GAN) | [Model]() |
- Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection - TIP |  | - | [Model]() |
- Contracting skeletal kinematics for human-related video anomaly detection - PR |  | [](https://github.com/aleflabo/COSKAD) | [Model]() |
- A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection - WACV |  | [](https://github.com/AnasEmad11/C2FPL) | [Model]() |
- Ada-VAD: Domain Adaptable Video Anomaly Detection - VAD) | [Model]() |
- Cross-modality integration framework with prediction, perception and discrimination for video anomaly detection - NN |  | - | [Model]() |
- MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection - CVPR |  | [](https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection) | [Model]() |
- Memory-enhanced appearance-motion consistency framework for video anomaly detection - CC |  | - | [Model]() |
- Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks - SNCS |  | - | [Model]() |
- Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection - IA |  | - | [Model]() |
- Anomaly detection in surveillance videos using deep autoencoder - IJIT |  | - | [Model]() |
- CVAD-GAN: Constrained video anomaly detection via generative adversarial network - IVC |  | [](https://github.com/Rituraj-ksi/CVAD-GAN/tree/main) | [Model]() |
- Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors - CVPR |  | [](https://github.com/ristea/aed-mae) | [Model]() |
- VADiffusion: Compressed Domain Information Guided Conditional Diffusion for Video Anomaly Detection - ITCaSfV |  | [](https://github.com/LHaoooo/VADiffusion) | [Model]() |
- A deep learning-assisted visual attention mechanism for anomaly detection in videos - MTnA |  | - | [Model]() |
- Delving into CLIP latent space for Video Anomaly Recognition - CVnIU |  | [](https://github.com/lucazanella/AnomalyCLIP) | [Model]() |
- An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction - WACV |  | [](https://github.com/TeCSAR-UNCC/TSGAD) | [Model]() |
- Holistic Representation Learning for Multitask Trajectory Anomaly Detection - WACV |  | [](https://github.com/alexandrosstergiou/TrajREC) | [Model]() |
- Deep learning based anomaly detection in real‑time video - MTnA |  | - | [Model]() |
- DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection - NC |  | - | [Model]() |
- ANOMALY DETECTION IN SATELLITE VIDEOS USING DIFFUSION MODELS - WMSP |  | [](https://github.com/a04101999/Anomaly-Detection-in-Satellite-Videos-using-Diffusion-Models) | [Model]() |
- Evolving graph-based video crowd anomaly detection - TVC |  | - | [Model]() |
- Feature Reconstruction With Disruption for Unsupervised Video Anomaly Detection - IToMs |  | [](https://github.com/tcc-power/FRD-unsupervised-video-anomaly-detection) | [Model]() |
- Cognition Guided Video Anomaly Detection Framework for Surveillance Services - IEoSC |  | [](https://github.com/zmh0124/CG-VAD) | [Model]() |
- An informative dual ForkNet for video anomaly detection - NN |  | - | [Model]() |
- MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model - PR |  | - | [Model]() |
- Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models - ECCV |  | [](https://github.com/Yuchen413/AnomalyRuler) | [Model]() |
- Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach - CVPR |  | [](https://github.com/rayush7/unified_PA) | [Model]() |
- Video anomaly detection with both normal and anomaly memory modules - TVC |  | [](https://github.com/SVIL2024/Pseudo-Anomaly-MemAE) | [Model]() |
- Video anomaly detection based on multi-scale optical flow spatio-temporal enhancement and normality mining - MLC |  | - | [Model]() |
- Generate anomalies from normal: a partial pseudo-anomaly augmented approach for video anomaly detection - TVC |  | [](https://github.com/OctCjy/GenerateAnomaliesFromNormal) | [Model]() |
- CroSA: Unsupervised domain adaptation abnormal behavior detection via cross-space alignment - ESWA |  | - | [Model]() |
- A novel spatio-temporal memory network for video anomaly detection - MTnA |  | - | [Model]() |
- AADC-Net: A Multimodal Deep Learning Framework for Automatic Anomaly Detection in Real-Time Surveillance - TIM |  | - | [Model]() |
- STAD-AI: Spatio-Temporal Anomaly Detection in Videos with Attentive Dual-Stage Integration - N |  | - | [Model]() |
- A Region based Salient Stacking Optimized Detector (ReSOD) for an effective anomaly detection and video tracking in surveillance systems - N |  | - | [Model]() |
- Prototype-guided and dynamic-aware video anomaly detection - NN |  | - | [Model]() |
- AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection - arXiv |  | - | [Model]() |
- VADMamba: Exploring State Space Models for Fast Video Anomaly Detection - arXiv |  | [](https://github.com/jLooo/VADMamba) | [Model]() |
- STVAD: A Lightweight Spatio–Temporal Attention Network for Video Anomaly Detection - TCSS |  | - | [Model]() |
- Video anomaly detection with motion and appearance guided patch diffusion model - AAAI |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- Fast video anomaly detection via context-aware shortcut exploration and abnormal feature distance learning - PR |  | [](https://github.com/vt-le/VideoAnomalyDection/blob/main) | [Model]() |
- AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM - AnyAnomaly) | [Model]() |
- Video Anomaly Detection via self-supervised and spatio-temporal proxy tasks learning - PR |  | - | [Model]() |
- A multi-memory-augmented network with a curvy metric method for video anomaly detection - NN |  | - | [Model]() |
- Delving Into Instance Modeling for Weakly Supervised Video Anomaly Detection - TCSV |  | - | [Model]() |
- A lightweight video anomaly detection model with weak supervision and adaptive instance selection - N |  | - | [Model]() |
- Retrieving and Reasoning: Multivariate Feature and Attribute Cooperation for Video Anomaly Detection - I_SPL |  | - | [Model]() |
- FDC-Net: foreground dynamic capture with deep feature enhancement for video anomaly detection - MS |  | - | [Model]() |
- Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling - IE |  | - | [Model]() |
- Learning dual updatable memory modules for video anomaly detection - MS |  | - | [Model]() |
- Time-Efficient Video Anomaly Detection With Parallel Computing and Twice-Reconstruction - ISJ |  | - | [Model]() |
- Crowd Anomaly Detection From Drone and Ground - IE |  | [](https://cadg24.github.io/home/) | [Model]() |
- Spatial–temporal sequential network for anomaly detection based on long short-term magnitude representation - InVC |  | - | [Model]() |
- Probabilistic memory auto-encoding network for abnormal behavior detection in surveillance video - NN |  | - | [Model]() |
- VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models - CVPR |  | [](https://github.com/vera-framework/VERA) | [Model]() |
- A video anomaly detection framework based on semantic consistency and multi-attribute feature complementarity - PR |  | [](https://github.com/jzt-dongli/SC-MAFC) | [Model]() |
- MSTAgent-VAD: Multi-scale video anomaly detection using time agent mechanism for segments’ temporal context mining - ESWA |  | - | [Model]() |
- PLOVAD: Prompting Vision-Language Models for Open Vocabulary Video Anomaly Detection - TCSVT |  | [](https://github.com/ctX-u/PLOVAD) | [Model]() |
- UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks - AAAI |  | [](https://github.com/YBQian-Roy/UCF-Crime-DVS) | [Model]() |
- CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos - TIP |  | - | [Model]() |
- Autoregressive Denoising Score Matching is a Good Video Anomaly Detector - ArXiv |  | [](https://github.com/Bbeholder/ADSM) | [Model]() |
- MMVAD: A vision–language model for cross-domain video anomaly detection with contrastive learning and scale-adaptive frame segmentation - ESWA |  | - | [Model]() |
- Dual-Stage attention mechanism for robust video anomaly detection and localization - SIVP |  | - | [Model]() |
- A novel video anomaly detection using hybrid sand cat Swarm optimization with backpropagation neural network by UCSD Ped 1 dataset - ICASSP |  | - | [Model]() |
- DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection - ArXiv |  | - | [Model]() |
- Deep Learning for Anomaly Detection: A CNN-LSTM Autoencoder Approach - CACCT |  | - | [Model]() |
- Human pose feature enhancement for human anomaly detection and tracking - IT |  | - | [Model]() |
- Graph-Jigsaw Conditioned Diffusion Model for Skeleton-Based Video Anomaly Detection - WACV |  | - | [Model]() |
- Semi-supervised Video Anomaly Detection With Compact Deformable 3D Convolution - ICASSP |  | - | [Model]() |
- Time-Efficient Video Anomaly Detection With Parallel Computing and Twice-Reconstruction - SJ |  | - | [Model]() |
- Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks - SNCS |  | - | [Model]() |
- Anomaly detection in surveillance videos using deep autoencoder - IJIT |  | - | [Model]() |
- A deep learning-assisted visual attention mechanism for anomaly detection in videos - MTnA |  | - | [Model]() |
- Deep learning based anomaly detection in real‑time video - MTnA |  | - | [Model]() |
- MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model - PR |  | - | [Model]() |
- Video anomaly detection with both normal and anomaly memory modules - TVC |  | [](https://github.com/SVIL2024/Pseudo-Anomaly-MemAE) | [Model]() |
- Human pose feature enhancement for human anomaly detection and tracking - IT |  | - | [Model]() |
- Video anomaly detection based on multi-scale optical flow spatio-temporal enhancement and normality mining - MLC |  | - | [Model]() |
- Generate anomalies from normal: a partial pseudo-anomaly augmented approach for video anomaly detection - TVC |  | [](https://github.com/OctCjy/GenerateAnomaliesFromNormal) | [Model]() |
- A novel spatio-temporal memory network for video anomaly detection - MTnA |  | - | [Model]() |
- Video anomaly detection with motion and appearance guided patch diffusion model - AAAI |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction - MS |  | - | [Model]() |
- FDC-Net: foreground dynamic capture with deep feature enhancement for video anomaly detection - MS |  | - | [Model]() |
- Learning dual updatable memory modules for video anomaly detection - MS |  | - | [Model]() |
- Dual-Stage attention mechanism for robust video anomaly detection and localization - SIVP |  | - | [Model]() |
- Generate anomalies from normal: a partial pseudo-anomaly augmented approach for video anomaly detection - TVC |  | [](https://github.com/OctCjy/GenerateAnomaliesFromNormal) | [Model]() |
- A novel spatio-temporal memory network for video anomaly detection - MTnA |  | - | [Model]() |
- Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks - SNCS |  | - | [Model]() |
- Anomaly detection in surveillance videos using deep autoencoder - IJIT |  | - | [Model]() |
- A deep learning-assisted visual attention mechanism for anomaly detection in videos - MTnA |  | - | [Model]() |
- Deep learning based anomaly detection in real‑time video - MTnA |  | - | [Model]() |
- MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model - PR |  | - | [Model]() |
- Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models - ECCV |  | [](https://github.com/Yuchen413/AnomalyRuler) | [Model]() |
- Video anomaly detection with both normal and anomaly memory modules - TVC |  | [](https://github.com/SVIL2024/Pseudo-Anomaly-MemAE) | [Model]() |
- Video anomaly detection based on multi-scale optical flow spatio-temporal enhancement and normality mining - MLC |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction - MS |  | - | [Model]() |
- FDC-Net: foreground dynamic capture with deep feature enhancement for video anomaly detection - MS |  | - | [Model]() |
- Learning dual updatable memory modules for video anomaly detection - MS |  | - | [Model]() |
- Dual-Stage attention mechanism for robust video anomaly detection and localization - SIVP |  | - | [Model]() |
- Human pose feature enhancement for human anomaly detection and tracking - IT |  | - | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- Video anomaly detection guided by clustering learning - PR |  | [](https://github.com/Bun-TianYi/Video-anomaly-detection-guided-by-clustering-learning) | [Model]() |
- Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models - ECCV |  | [](https://github.com/Yuchen413/AnomalyRuler) | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
- HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-net - AI |  | [](https://github.com/vt-le/HSTforU) | [Model]() |
-
Weakly Supervised VAD
- BatchNorm-Based Weakly Supervised Video Anomaly Detection - CVPR |  | [](https://github.com/cool-xuan/BN-WVAD) | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection - AAAI |  | [](https://github.com/nwpu-zxr/VadCLIP) | [Model]() |
- Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts - ACM ICoM |  | - | [Model]() |
- Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection - IRoIP |  | [](https://github.com/yujiangpu20/PEL4VAD) | [Model]() |
- Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection - IToIP |  | [](https://github.com/yujiangpu20/PEL4VAD) | [Model]() |
- Real-Time Weakly Supervised Video Anomaly Detection - WACV |  | - | [Model]() |
- Overlooked Video Classification in Weakly Supervised Video Anomaly Detection - WACV |  | [](https://github.com/wjtan99/BERT_Anomaly_Video_Classification) | [Model]() |
- Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection - CVPR |  | [](https://github.com/Junxi-Chen/PE-MIL) | [Model]() |
- Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection - CVPR |  | - | [Model]() |
- GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection - CVPR |  | [](https://github.com/pipixin321/GlanceVAD) | [Model]() |
- TDSD: Text-Driven Scene-Decoupled Weakly Supervised Video Anomaly Detection - CVPR |  | [](https://github.com/shengyangsun/TDSD) | [Model]() |
- A Multi-Head Approach with Shuffled Segments for Weakly-Supervised Video Anomaly Detection - WACV |  | - | [Model]() |
- Diffusion-based normality pre-training for weakly supervised video anomaly detection - ESWA |  | - | [Model]() |
- OE-CTST: Outlier-Embedded Cross Temporal Scale Transformer for Weakly-supervised Video Anomaly Detection - WACV |  | - | [Model]() |
- VPE-WSVAD: Visual prompt exemplars for weakly-supervised video anomaly detection - KBS |  | [](https://github.com/vt-le/VideoAnomalyDection/blob/main) | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Semantic-driven dual consistency learning for weakly supervised video anomaly detection - PR |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Positive and unlabeled learning on generating strategy for weakly anomaly detection - SInVP |  | - | [Model]() |
- Transformer-enabled weakly supervised abnormal event detection in intelligent video surveillance systems - EAAI |  | [](https://github.com/Shalmiyapaulraj78/STHTAM-VAD) | [Model]() |
- Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping - TII |  | - | [Model]() |
- ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications - ArXiv |  | [](https://github.com/modadundun/ProDisc-VAD) | [Model]() |
- Anomaly-aware self-supervised feature learning for weakly supervised video anomaly detection - CVIU |  | - | [Model]() |
- Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection - CVPR |  | - | [Model]() |
- Federated weakly-supervised video anomaly detection with mixture of local-to-global experts - IF |  | - | [Model]() |
- Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection - WACV |  | - | [Model]() |
- Dual Distillation Fusion for Weakly Supervised Anomaly Detection in Surveillance Videos - TCSVT |  | - | [Model]() |
- Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping - TII |  | - | [Model]() |
- Open-Vocabulary Video Anomaly Detection - CVPR |  | - | [Model]() |
- Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection - CVPR |  | - | [Model]() |
-
Surveys
- Video anomaly detection: A systematic review of issues and prospects - N |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions - AIR |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions - IToETiCI |  | - | [Model]() |
- A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance - PnUC |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Networking Systems for Video Anomaly Detection: A Tutorial and Survey - X |  | [](https://github.com/fdjingliu/NSVAD) | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A Survey on Diffusion Models for Anomaly Detection
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions - AIR |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A critical study on the recent deep learning based semi-supervised video anomaly detection methods - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions - AIR |  | - | [Model]() |
- Deep learning for video anomaly detection: A review - | [Model]() |
- Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions - AIR |  | - | [Model]() |
- A Survey on Diffusion Models for Anomaly Detection - ArXiv |  | [](https://github.com/fdjingliu/DMAD) | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions - AIR |  | - | [Model]() |
- A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection - MTnA |  | - | [Model]() |
- Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions - AIR |  | - | [Model]() |
-
New VAD Datasets
- Advancing Video Anomaly Detection: A Concise Review and a New Dataset - X |  | [](https://msad-dataset.github.io/) | [Model](-) |
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