# AVID: Adversarial Visual Irregularity Detection

arXiv: https://arxiv.org/abs/1805.09521 · HTML: https://mlsanity.com/paper/1805.09521/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2018.

## Built on by (most cited first, 19 of 19)

- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [Self-Supervised Masking for Unsupervised Anomaly Detection and Localization](https://mlsanity.com/paper/2205.06568/) (2022)
- [Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection](https://mlsanity.com/paper/2209.12148/) (2022)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2203.01671/) (2022)
- [Video Anomaly Detection in 10 Years: A Survey and Outlook](https://mlsanity.com/paper/2405.19387/) (2024)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Curved Geometric Networks for Visual Anomaly Recognition](https://mlsanity.com/paper/2208.01188/) (2022)
- [Unsupervised Two-Stage Anomaly Detection](https://mlsanity.com/paper/2103.11671/) (2021)
- [Visual Anomaly Detection for Images: A Survey](https://mlsanity.com/paper/2109.13157/) (2021)
- [Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples](https://mlsanity.com/paper/2210.17258/) (2022)
- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [A Prototype-Based Neural Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2310.02576/) (2023)
- [Data Invariants to Understand Unsupervised Out-of-Distribution Detection](https://mlsanity.com/paper/2111.13362/) (2021)
- [Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies](https://mlsanity.com/paper/2103.10502/) (2021)
- [Looking at the whole picture: constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2109.00482/) (2021)
- [Identifying Spatio-Temporal Drivers of Extreme Events](https://mlsanity.com/paper/2410.24075/) (2024)

## Abstract (arXiv)

Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity Inpainter, and the other works as a patch-level Detector. After an adversarial self-supervised training, in which I tries to fool D into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets show that our method can outperform the state-of-the-art and fine-segment the irregularity.