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

arXiv: https://arxiv.org/abs/2211.15098 · HTML: https://mlsanity.com/paper/2211.15098/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 49 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Video Action Transformer Network](https://mlsanity.com/paper/1812.02707/) (2018)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision](https://mlsanity.com/paper/2007.04687/) (2020)
- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection](https://mlsanity.com/paper/1903.07256/) (2019)
- [MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection](https://mlsanity.com/paper/2104.01633/) (2021)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection](https://mlsanity.com/paper/2011.12077/) (2020)
- [LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention](https://mlsanity.com/paper/2004.01389/) (2020)
- [GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection](https://mlsanity.com/paper/1908.05884/) (2019)
- [Few-shot Scene-adaptive Anomaly Detection](https://mlsanity.com/paper/2007.07843/) (2020)
- [Motion-Aware Feature for Improved Video Anomaly Detection](https://mlsanity.com/paper/1907.10211/) (2019)
- [Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning](https://mlsanity.com/paper/2104.07268/) (2021)

## Built on by (most cited first, 30 of 89)

- [Harnessing Large Language Models for Training-free Video Anomaly Detection](https://mlsanity.com/paper/2404.01014/) (2024)
- [Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity](https://mlsanity.com/paper/2412.06171/) (2024)
- [Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM](https://mlsanity.com/paper/2406.12235/) (2024)
- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2404.08531/) (2024)
- [Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges](https://mlsanity.com/paper/2309.13925/) (2023)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Delving into CLIP latent space for Video Anomaly Recognition](https://mlsanity.com/paper/2310.02835/) (2023)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2310.12790/) (2023)
- [VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models](https://mlsanity.com/paper/2412.01095/) (2024)
- [VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2505.23504/) (2025)
- [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023)
- [Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion](https://mlsanity.com/paper/2410.01506/) (2024)
- [VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree](https://mlsanity.com/paper/2510.22693/) (2025)
- [Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention](https://mlsanity.com/paper/2309.16309/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection](https://mlsanity.com/paper/2505.15205/) (2025)
- [A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis](https://mlsanity.com/paper/2511.00962/) (2025)
- [MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection](https://mlsanity.com/paper/2510.21449/) (2025)
- [Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection](https://mlsanity.com/paper/2503.03562/) (2025)
- [HiProbe-VAD: Video Anomaly Detection via Hidden States Probing in Tuning-Free Multimodal LLMs](https://mlsanity.com/paper/2507.17394/) (2025)
- [Aligning Effective Tokens with Video Anomaly in Large Language Models](https://mlsanity.com/paper/2508.06350/) (2025)
- [Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting](https://mlsanity.com/paper/2510.02155/) (2025)
- [Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera](https://mlsanity.com/paper/2411.10945/) (2024)
- [Cross-Domain Learning for Video Anomaly Detection with Limited Supervision](https://mlsanity.com/paper/2408.05191/) (2024)
- [Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark](https://mlsanity.com/paper/2408.14329/) (2024)
- [Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer](https://mlsanity.com/paper/2408.15185/) (2024)
- [Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2508.06318/) (2025)
- [ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition](https://mlsanity.com/paper/2502.00156/) (2025)

## Abstract (arXiv)

Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.