[2605.02912] Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.02912 (cs)
[Submitted on 7 Apr 2026]
Title:Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
Authors:Sakshi Agarwal, Aishik Konwer, Ankit Parag Shah
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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.
Comments: under review at conference
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.02912 [cs.CV]
(or arXiv:2605.02912v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.02912
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arXiv-issued DOI via DataCite
Submission history
From: Aishik Konwer [view email] [v1] Tue, 7 Apr 2026 20:15:15 UTC (6,482 KB)
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