Rethinking Metrics and Benchmarks of Video Anomaly Detection
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Zihao, Wu, Xiaoyu, Li, Wenna, Yang, Linlin, Wang, Shengjin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions
by: Liu, Zihao, et al.
Published: (2026)
by: Liu, Zihao, et al.
Published: (2026)
MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding
by: Zhu, Zhiyi, et al.
Published: (2025)
by: Zhu, Zhiyi, et al.
Published: (2025)
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
by: Wu, Peng, et al.
Published: (2023)
by: Wu, Peng, et al.
Published: (2023)
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
by: Pu, Yujiang, et al.
Published: (2023)
by: Pu, Yujiang, et al.
Published: (2023)
Language-guided Open-world Video Anomaly Detection under Weak Supervision
by: Liu, Zihao, et al.
Published: (2025)
by: Liu, Zihao, et al.
Published: (2025)
No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection
by: Dai, Zunkai, et al.
Published: (2026)
by: Dai, Zunkai, et al.
Published: (2026)
Dynamic Object Queries for Transformer-based Incremental Object Detection
by: Zhang, Jichuan, et al.
Published: (2024)
by: Zhang, Jichuan, et al.
Published: (2024)
Deep Learning for Video Anomaly Detection: A Review
by: Wu, Peng, et al.
Published: (2024)
by: Wu, Peng, et al.
Published: (2024)
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts
by: Wu, Peng, et al.
Published: (2024)
by: Wu, Peng, et al.
Published: (2024)
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
by: Xie, Guoyang, et al.
Published: (2023)
by: Xie, Guoyang, et al.
Published: (2023)
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection
by: Wu, Ruituo, et al.
Published: (2024)
by: Wu, Ruituo, et al.
Published: (2024)
Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly
by: Du, Hang, et al.
Published: (2024)
by: Du, Hang, et al.
Published: (2024)
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
by: Jiang, Xi, et al.
Published: (2024)
by: Jiang, Xi, et al.
Published: (2024)
GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection
by: Cai, Suhang, et al.
Published: (2025)
by: Cai, Suhang, et al.
Published: (2025)
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection
by: Lappas, Demetris, et al.
Published: (2024)
by: Lappas, Demetris, et al.
Published: (2024)
Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
by: Pazho, Armin Danesh, et al.
Published: (2024)
by: Pazho, Armin Danesh, et al.
Published: (2024)
Networking Systems for Video Anomaly Detection: A Tutorial and Survey
by: Liu, Jing, et al.
Published: (2024)
by: Liu, Jing, et al.
Published: (2024)
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
by: Wu, Jingqi, et al.
Published: (2025)
by: Wu, Jingqi, et al.
Published: (2025)
Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
by: Yang, Nianzu, et al.
Published: (2025)
by: Yang, Nianzu, et al.
Published: (2025)
Shot Sequence Ordering for Video Editing: Benchmarks, Metrics, and Cinematology-Inspired Computing Methods
by: Li, Yuzhi, et al.
Published: (2025)
by: Li, Yuzhi, et al.
Published: (2025)
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
by: Du, Hang, et al.
Published: (2024)
by: Du, Hang, et al.
Published: (2024)
MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection
by: Zhao, Xiran, et al.
Published: (2026)
by: Zhao, Xiran, et al.
Published: (2026)
Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks
by: Li, Jie, et al.
Published: (2025)
by: Li, Jie, et al.
Published: (2025)
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development
by: Lei, Tianwu, et al.
Published: (2024)
by: Lei, Tianwu, et al.
Published: (2024)
Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning
by: Yang, Tao, et al.
Published: (2025)
by: Yang, Tao, et al.
Published: (2025)
RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection
by: Wang, Zhuo, et al.
Published: (2025)
by: Wang, Zhuo, et al.
Published: (2025)
Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions
by: Barusco, Manuel, et al.
Published: (2026)
by: Barusco, Manuel, et al.
Published: (2026)
Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting
by: Zou, Shu, et al.
Published: (2025)
by: Zou, Shu, et al.
Published: (2025)
Rethinking The Uniformity Metric in Self-Supervised Learning
by: Fang, Xianghong, et al.
Published: (2024)
by: Fang, Xianghong, et al.
Published: (2024)
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics
by: Tusher, Md. Barkat Ullah, et al.
Published: (2025)
by: Tusher, Md. Barkat Ullah, et al.
Published: (2025)
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring
by: Cholopoulou, Eirini, et al.
Published: (2024)
by: Cholopoulou, Eirini, et al.
Published: (2024)
Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation
by: Cai, Yeming, et al.
Published: (2025)
by: Cai, Yeming, et al.
Published: (2025)
Rethinking Video Human-Object Interaction: Set Prediction over Time for Unified Detection and Anticipation
by: Luo, Yuanhao, et al.
Published: (2026)
by: Luo, Yuanhao, et al.
Published: (2026)
Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection
by: Zia, Ali, et al.
Published: (2026)
by: Zia, Ali, et al.
Published: (2026)
H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding
by: Wu, Qi, et al.
Published: (2025)
by: Wu, Qi, et al.
Published: (2025)
Video-Bench: Human-Aligned Video Generation Benchmark
by: Han, Hui, et al.
Published: (2025)
by: Han, Hui, et al.
Published: (2025)
SeqBench: Benchmarking Sequential Narrative Generation in Text-to-Video Models
by: Tang, Zhengxu, et al.
Published: (2025)
by: Tang, Zhengxu, et al.
Published: (2025)
Knowledge-Guided Textual Reasoning for Explainable Video Anomaly Detection via LLMs
by: Lee, Hari
Published: (2025)
by: Lee, Hari
Published: (2025)
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
by: Karami, Ali, et al.
Published: (2024)
by: Karami, Ali, et al.
Published: (2024)
Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance
by: Sun, Shengyang, et al.
Published: (2026)
by: Sun, Shengyang, et al.
Published: (2026)
Similar Items
-
ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions
by: Liu, Zihao, et al.
Published: (2026) -
MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding
by: Zhu, Zhiyi, et al.
Published: (2025) -
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
by: Wu, Peng, et al.
Published: (2023) -
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
by: Pu, Yujiang, et al.
Published: (2023) -
Language-guided Open-world Video Anomaly Detection under Weak Supervision
by: Liu, Zihao, et al.
Published: (2025)