# AVAR-Net: A Lightweight Audio-Visual Anomaly Recognition Framework with a Benchmark Dataset

arXiv: https://arxiv.org/abs/2510.13630 · HTML: https://mlsanity.com/paper/2510.13630/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [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)
- [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)
- [VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2308.11681/) (2023)
- [Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2302.05160/) (2023)
- [MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2211.15098/) (2022)
- [Harnessing Large Language Models for Training-free Video Anomaly Detection](https://mlsanity.com/paper/2404.01014/) (2024)
- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Ambient Sound Provides Supervision for Visual Learning](https://mlsanity.com/paper/1608.07017/) (2016)
- [Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2212.04090/) (2022)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)
- [Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection](https://mlsanity.com/paper/2207.05500/) (2022)
- [Learning to Detect Violent Videos using Convolutional Long Short-Term Memory](https://mlsanity.com/paper/1709.06531/) (2017)
- [Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network](https://mlsanity.com/paper/1806.00746/) (2018)
- [Audio-visual Representation Learning for Anomaly Events Detection in Crowds](https://mlsanity.com/paper/2110.14862/) (2021)
- [Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space](https://mlsanity.com/paper/2305.18797/) (2023)
- [Beyond Euclidean: Dual-Space Representation Learning for Weakly Supervised Video Violence Detection](https://mlsanity.com/paper/2409.19252/) (2024)
- [CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention](https://mlsanity.com/paper/2404.18952/) (2024)
- [Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond](https://mlsanity.com/paper/2308.14753/) (2023)
- [Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection](https://mlsanity.com/paper/2409.11223/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-Modal Anomaly Detection: A Survey](https://mlsanity.com/paper/2608.24937/) (2026)

## Abstract (arXiv)

Anomaly recognition plays a vital role in surveillance, transportation, healthcare, and public safety. However, most existing approaches rely solely on visual data, making them unreliable under challenging conditions such as occlusion, low illumination, and adverse weather. Moreover, the absence of large-scale synchronized audio-visual datasets has hindered progress in multimodal anomaly recognition. To address these limitations, this study presents AVAR-Net, a lightweight and efficient audio-visual anomaly recognition framework designed for real-world environments. AVAR-Net consists of four main modules: an audio feature extractor, a video feature extractor, fusion strategy, and a sequential pattern learning network that models cross-modal relationships for anomaly recognition. Specifically, the Wav2Vec2 model extracts robust temporal features from raw audio, while MobileViT captures both local and global visual representations from video frames. An early fusion mechanism combines these modalities, and a Multi-Stage Temporal Convolutional Network (MTCN) model that learns long-range temporal dependencies within the fused representation, enabling robust spatiotemporal reasoning. A novel Visual-Audio Anomaly Recognition (VAAR) dataset, is also introduced, serving as a medium-scale benchmark containing 3,000 real-world videos with synchronized audio across ten diverse anomaly classes. Experimental evaluations demonstrate that AVAR-Net achieves 89.29% accuracy on VAAR and 88.56% Average Precision on the XD-Violence dataset, improving Average Precision by 2.8% over existing state-of-the-art methods. These results highlight the effectiveness, efficiency, and generalization capability of the proposed framework, as well as the utility of VAAR as a benchmark for advancing multimodal anomaly recognition research.