# Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning

arXiv: https://arxiv.org/abs/2104.07268 · HTML: https://mlsanity.com/paper/2104.07268/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection](https://mlsanity.com/paper/1903.07256/) (2019)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [W-TALC: Weakly-supervised Temporal Activity Localization and Classification](https://mlsanity.com/paper/1807.10418/) (2018)
- [C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1904.05647/) (2019)
- [3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization](https://mlsanity.com/paper/1908.08216/) (2019)
- [Motion-Aware Feature for Improved Video Anomaly Detection](https://mlsanity.com/paper/1907.10211/) (2019)

## Built on by (most cited first, 30 of 44)

- [MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2211.15098/) (2022)
- [CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2212.05136/) (2022)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2306.14451/) (2023)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Towards Open Set Video Anomaly Detection](https://mlsanity.com/paper/2208.11113/) (2022)
- [Weakly Supervised Anomaly Detection: A Survey](https://mlsanity.com/paper/2302.04549/) (2023)
- [Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection](https://mlsanity.com/paper/2203.12121/) (2022)
- [UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks](https://mlsanity.com/paper/2503.12905/) (2025)
- [Anomaly Detection in Video Sequences: A Benchmark and Computational Model](https://mlsanity.com/paper/2106.08570/) (2021)
- [Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2303.18044/) (2023)
- [Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection](https://mlsanity.com/paper/2208.05636/) (2022)
- [Aligning Effective Tokens with Video Anomaly in Large Language Models](https://mlsanity.com/paper/2508.06350/) (2025)
- [Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos](https://mlsanity.com/paper/2202.06503/) (2022)
- [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)
- [Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2401.13551/) (2024)
- [Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection](https://mlsanity.com/paper/2301.07923/) (2023)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [RefineVAD: Semantic-Guided Feature Recalibration for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2511.13204/) (2025)
- [Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection](https://mlsanity.com/paper/2505.13123/) (2025)
- [Discriminative-Generative Dual Memory Video Anomaly Detection](https://mlsanity.com/paper/2104.14430/) (2021)
- [Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance](https://mlsanity.com/paper/2212.08506/) (2022)
- [Multiple Instance Learning for Cheating Detection and Localization in Online Examinations](https://mlsanity.com/paper/2402.06107/) (2024)
- [Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection](https://mlsanity.com/paper/2409.11223/) (2024)
- [A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection](https://mlsanity.com/paper/2310.05330/) (2023)
- [Identifying Spatio-Temporal Drivers of Extreme Events](https://mlsanity.com/paper/2410.24075/) (2024)
- [ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications](https://mlsanity.com/paper/2505.02179/) (2025)
- [TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detection](https://mlsanity.com/paper/2511.00580/) (2025)
- [CADE: Continual Weakly-supervised Video Anomaly Detection with Ensembles](https://mlsanity.com/paper/2512.06840/) (2025)

## Abstract (arXiv)

Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset