# An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?

arXiv: https://arxiv.org/abs/2605.25806 · HTML: https://mlsanity.com/paper/2605.25806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 22 of 22)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [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)
- [MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection](https://mlsanity.com/paper/2104.01633/) (2021)
- [VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2308.11681/) (2023)
- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [Street Scene: A new dataset and evaluation protocol for video anomaly detection](https://mlsanity.com/paper/1902.05872/) (2019)
- [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)
- [CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2212.05136/) (2022)
- [GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection](https://mlsanity.com/paper/1908.05884/) (2019)
- [Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges](https://mlsanity.com/paper/2309.13925/) (2023)
- [Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey](https://mlsanity.com/paper/1901.08292/) (2019)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection](https://mlsanity.com/paper/2504.14221/) (2025)
- [AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios](https://mlsanity.com/paper/2410.14379/) (2024)

## Abstract (arXiv)

Women's safety and security are paramount for a modern society. Often, crimes scenes get recorded through low-resolution CCTV cameras limiting the efficiency of video anomaly detection (VAD) models. Despite substantial progress in VAD research, women-centric anomalies are still underrepresented in datasets as well as in models. Existing datasets primarily cover well-lit, high-resolution and close-shot videos that are inadequate to tackle critical anomalies such as chain snatching, stalking, inappropriate touch, and other subtle forms of crime against women. To address this, we present a new benchmark, referred to as ExtrAnom. It contains 1001 videos (both anomalies and normal) with four textual annotations; one human-generated and three LLM-generated. The videos are arranged in 5 different categories of crimes. The dataset comprises low-light (8%), low-resolution (13%), long-shot (15%), and daytime (64%) anomaly videos. It includes stalking (3.9%), chain snatching (17.6%), kidnapping (7.3%), assassinations (2.3%), harassment (18.9%), and normal (50%) videos. It is possible to perform cross-modal and VLM-based validations using ExtrAnom. We have benchmarked it against popular unimodal and multi-modal VAD datasets (e.g., XD-Violence, UCF-Crime, and UCA) and SOTA methods. Experiments reveal that existing datasets are insufficient to deal with women-centric anomalies. We believe ExtrAnom can fill this critical gap in VAD research.