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

arXiv: https://arxiv.org/abs/2310.17650 · HTML: https://mlsanity.com/paper/2310.17650/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [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)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [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)
- [Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm](https://mlsanity.com/paper/2004.07657/) (2020)
- [Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos](https://mlsanity.com/paper/1903.03295/) (2019)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection](https://mlsanity.com/paper/2011.12077/) (2020)
- [Toward Supervised Anomaly Detection](https://mlsanity.com/paper/1401.6424/) (2014)
- [GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection](https://mlsanity.com/paper/1908.05884/) (2019)
- [Part-based Pseudo Label Refinement for Unsupervised Person Re-identification](https://mlsanity.com/paper/2203.14675/) (2022)
- [Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification](https://mlsanity.com/paper/2106.06133/) (2021)
- [Anomaly Detection in Medical Imaging -- A Mini Review](https://mlsanity.com/paper/2108.11986/) (2021)
- [Subspace Support Vector Data Description](https://mlsanity.com/paper/1802.03989/) (2018)
- [Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2105.14737/) (2021)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies](https://mlsanity.com/paper/2203.13716/) (2022)
- [Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection](https://mlsanity.com/paper/2104.14770/) (2021)
- [Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2103.15973/) (2021)

## Built on by (most cited first, 13 of 13)

- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline](https://mlsanity.com/paper/2404.00847/) (2024)
- [FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data](https://mlsanity.com/paper/2411.16110/) (2024)
- [Procedural Mistake Detection via Action Effect Modeling](https://mlsanity.com/paper/2512.03474/) (2025)
- [Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2401.13551/) (2024)
- [Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation](https://mlsanity.com/paper/2506.11777/) (2025)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models](https://mlsanity.com/paper/2605.12725/) (2026)
- [Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer](https://mlsanity.com/paper/2408.13643/) (2024)
- [Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/2507.01924/) (2025)
- [RobustA: Robust Anomaly Detection in Multimodal Data](https://mlsanity.com/paper/2511.07276/) (2025)
- [Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection](https://mlsanity.com/paper/2603.13374/) (2026)

## Abstract (arXiv)

Detection of anomalous events in videos is an important problem in applications such as surveillance. Video anomaly detection (VAD) is well-studied in the one-class classification (OCC) and weakly supervised (WS) settings. However, fully unsupervised (US) video anomaly detection methods, which learn a complete system without any annotation or human supervision, have not been explored in depth. This is because the lack of any ground truth annotations significantly increases the magnitude of the VAD challenge. To address this challenge, we propose a simple-but-effective two-stage pseudo-label generation framework that produces segment-level (normal/anomaly) pseudo-labels, which can be further used to train a segment-level anomaly detector in a supervised manner. The proposed coarse-to-fine pseudo-label (C2FPL) generator employs carefully-designed hierarchical divisive clustering and statistical hypothesis testing to identify anomalous video segments from a set of completely unlabeled videos. The trained anomaly detector can be directly applied on segments of an unseen test video to obtain segment-level, and subsequently, frame-level anomaly predictions. Extensive studies on two large-scale public-domain datasets, UCF-Crime and XD-Violence, demonstrate that the proposed unsupervised approach achieves superior performance compared to all existing OCC and US methods , while yielding comparable performance to the state-of-the-art WS methods.