#计算机科学#Papers for Video Anomaly Detection, released codes collection, Performance Comparision.
ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.
#计算机科学#Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]
🔥 🔥 🔥 [NeurIPS 2024] Official Implementation of Hawk: Learning to Understand Open-World Video Anomalies
Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"
This is an official implementation for "Attention-based Residual Autoencoder for Video Anomaly Detection".
#计算机科学#Useful Toolbox for Anomaly Detection
An Attribute-based Method for Video Anomaly Detection (TMLR 2025)
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. Oral paper in ACM Multimedia 2020.
[CVPR 2025 Highlight] Official implementation of "Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity"
Official code for AAAI2023 paper "Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection"
#Awesome#A curated collection of papers, code, datasets, and utilities for Video Anomaly Detection, updated every Friday.
Frame level anomaly detection and localization in videos using auto-encoders
#计算机科学#Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Pytorch Re-implement of ano_pre_cvpr2018, flownet2 / lite-flownet used.
This is an official implement for "HSTforU: Anomaly Detection in Aerial and Ground-based Videos with Hierarchical Spatio-Temporal Transformer for U-net"
#计算机科学#A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
This is the code repo for the paper VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models (CVPR 2025).
#计算机科学#[ICME 2025] Official implementation of "GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection"
[ICIP 2023] Exploring Diffusion Models For Unsupervised Video Anomaly Detection