# Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection

arXiv: https://arxiv.org/abs/2203.12840 · HTML: https://mlsanity.com/paper/2203.12840/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 24 Mar 2022.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [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)
- [Learning with Average Top-k Loss](https://mlsanity.com/paper/1705.08826/) (2017)
- [Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders](https://mlsanity.com/paper/1510.01442/) (2015)
- [Learning Competitive and Discriminative Reconstructions for Anomaly Detection](https://mlsanity.com/paper/1903.07058/) (2019)

## Built on by (most cited first, 23 of 23)

- [CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2212.05136/) (2022)
- [Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2404.08531/) (2024)
- [Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2212.04090/) (2022)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Open-World Semantic Segmentation Including Class Similarity](https://mlsanity.com/paper/2403.07532/) (2024)
- [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023)
- [Cross-Domain Learning for Video Anomaly Detection with Limited Supervision](https://mlsanity.com/paper/2408.05191/) (2024)
- [Vision-Language Models Assisted Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2409.14109/) (2024)
- [Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2401.13551/) (2024)
- [Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation](https://mlsanity.com/paper/2405.04405/) (2024)
- [Learning Event Completeness for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2506.13095/) (2025)
- [Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning](https://mlsanity.com/paper/2603.00550/) (2026)
- [Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance](https://mlsanity.com/paper/2212.08506/) (2022)
- [Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection](https://mlsanity.com/paper/2403.01169/) (2024)
- [Open-World Panoptic Segmentation](https://mlsanity.com/paper/2412.12740/) (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)
- [TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance](https://mlsanity.com/paper/2411.11003/) (2024)
- [A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data](https://mlsanity.com/paper/2503.11097/) (2025)
- [GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2508.00312/) (2025)
- [Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2312.01764/) (2023)
- [GMFVAD: Using Grained Multi-modal Feature to Improve Video Anomaly Detection](https://mlsanity.com/paper/2510.20268/) (2025)
- [Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data](https://mlsanity.com/paper/2605.27644/) (2026)

## Abstract (arXiv)

Multiple-instance learning (MIL) provides an effective way to tackle the video anomaly detection problem by modeling it as a weakly supervised problem as the labels are usually only available at the video level while missing for frames due to expensive labeling cost. We propose to conduct novel Bayesian non-parametric submodular video partition (BN-SVP) to significantly improve MIL model training that can offer a highly reliable solution for robust anomaly detection in practical settings that include outlier segments or multiple types of abnormal events. BN-SVP essentially performs dynamic non-parametric hierarchical clustering with an enhanced self-transition that groups segments in a video into temporally consistent and semantically coherent hidden states that can be naturally interpreted as scenes. Each segment is assumed to be generated through a non-parametric mixture process that allows variations of segments within the same scenes to accommodate the dynamic and noisy nature of many real-world surveillance videos. The scene and mixture component assignment of BN-SVP also induces a pairwise similarity among segments, resulting in non-parametric construction of a submodular set function. Integrating this function with an MIL loss effectively exposes the model to a diverse set of potentially positive instances to improve its training. A greedy algorithm is developed to optimize the submodular function and support efficient model training. Our theoretical analysis ensures a strong performance guarantee of the proposed algorithm. The effectiveness of the proposed approach is demonstrated over multiple real-world anomaly video datasets with robust detection performance.