VANGUARD Framework Enhances Video Anomaly Detection with Reasoning and Grounding
A new framework named VANGUARD (Video Anomaly Understanding through Reasoning and Grounding) has been developed by researchers, combining anomaly classification, spatial grounding, and chain-of-thought reasoning in a single Vision-Language Model (VLM). Conventional Video Anomaly Detection (VAD) techniques typically depend on binary classification or outlier detection, which lack clarity and accurate localization. While VLMs provide extensive scene comprehension, they often fail to deliver dependable spatial grounding, resulting in fabricated bounding boxes. VANGUARD tackles this issue through a structured three-stage approach: initial classifier warmup using frozen backbone features, LoRA-adapted spatial grounding, and chain-of-thought generation. To address the challenge of sparse annotations in VAD benchmarks, a teacher-student annotation pipeline is utilized. This research is documented in arXiv:2605.02912.
Key facts
- VANGUARD unifies anomaly classification, spatial grounding, and chain-of-thought reasoning in a single VLM.
- Traditional VAD methods are limited to binary classification or outlier detection without interpretability.
- VLMs often produce hallucinated or geometrically invalid bounding boxes for object localization.
- The framework uses a three-stage curriculum: classifier warmup, LoRA-adapted spatial grounding, and chain-of-thought generation.
- A teacher-student annotation pipeline addresses sparse annotations in VAD benchmarks.
- The research is published on arXiv with ID 2605.02912.
- VANGUARD stands for Video Anomaly Understanding through Reasoning and Grounding.
- The approach aims to improve both interpretability and spatial precision in anomaly detection.
Entities
Institutions
- arXiv