Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
📰 ArXiv cs.AI
Learn how to improve video anomaly detection using multimodal large language models and reasoning-guided grounding, enhancing interpretability and spatial localization.
Action Steps
- Apply multimodal large language models to video anomaly detection tasks
- Implement reasoning-guided grounding to improve spatial localization and interpretability
- Use Vision-Language Models (VLMs) to enhance scene understanding
- Evaluate the performance of VANGUARD (Video Anomaly Detection with Grounding) on benchmark datasets
- Fine-tune the model using domain-specific data to adapt to various applications
Who Needs to Know This
Computer vision engineers and researchers can benefit from this approach to develop more accurate and explainable video anomaly detection systems. It can be applied in various fields such as surveillance, healthcare, and autonomous vehicles.
Key Insight
💡 Multimodal large language models can be used to improve video anomaly detection by providing interpretable reasoning and precise spatial localization.
Share This
Boost video anomaly detection with multimodal LLMs and reasoning-guided grounding! #AI #ComputerVision #VAD
Key Takeaways
Learn how to improve video anomaly detection using multimodal large language models and reasoning-guided grounding, enhancing interpretability and spatial localization.
Full Article
Title: Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
Abstract:
arXiv:2605.02912v1 Announce Type: cross Abstract: Video Anomaly Detection (VAD) has traditionally been framed as binary classification or outlier detection, providing neither interpretable reasoning nor precise spatial localization of anomalous events. While Vision-Language Models (VLMs) offer rich scene understanding, they struggle with reliable spatial grounding - often producing hallucinated or geometrically invalid bounding boxes when asked to localize objects. We propose VANGUARD (Video Ano
Abstract:
arXiv:2605.02912v1 Announce Type: cross Abstract: Video Anomaly Detection (VAD) has traditionally been framed as binary classification or outlier detection, providing neither interpretable reasoning nor precise spatial localization of anomalous events. While Vision-Language Models (VLMs) offer rich scene understanding, they struggle with reliable spatial grounding - often producing hallucinated or geometrically invalid bounding boxes when asked to localize objects. We propose VANGUARD (Video Ano
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