Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models

TL;DR

VANGUARD introduces a unified framework combining anomaly detection, spatial grounding, and reasoning in video analysis using multimodal large language models, achieving high accuracy and interpretability.

Contribution

It presents a novel three-stage training curriculum and a teacher-student annotation pipeline to enhance video anomaly detection with reasoning and grounding capabilities.

Findings

Achieves 94% ROC-AUC and 84% F1 on UCF-Crime.

Produces interpretable chain-of-thought explanations and spatial grounding.

Demonstrates strong zero-shot transfer to other datasets.

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 Anomaly Understanding through Reasoning and Grounding), a framework that unifies anomaly classification, spatial grounding, and chain-of-thought reasoning within a single VLM. VANGUARD introduces a three-stage curriculum that progressively layers training objectives: (1) classifier warmup on frozen backbone features, (2) LoRA-adapted spatial grounding, and (3) chain-of-thought generation. To overcome the sparse annotation typical of VAD benchmarks, we employ a teacher-student annotation pipeline in which a VLM (Qwen3-VL-4B) generates structured per-subclip reasoning trajectories based on manual annotations available from the UCA Dataset. Further, GroundingDINO provides bounding box supervision. On UCF-Crime, VANGUARD achieves 94% ROC-AUC with 84% F1 while simultaneously producing interpretable chain-of-thought explanations and spatial grounding of anomalous objects - capabilities absent from prior VAD methods. Ablations confirm that staged training outperforms monolithic optimization, and that structured reasoning acts as an implicit regularizer yielding more balanced predictions than classification-only fine-tuning. Zero-shot transfer to XD-Violence and ShanghaiTech demonstrates cross-domain generalization without target-domain adaptation.