Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
Video Anomaly Detection (VAD) has long been a challenge within computer vision, primarily framed as a binary classification or outlier detection task. This approach has often resulted in a lack of interpretable reasoning and precise spatial localization of anomalous events. Traditional methods struggle with reliable spatial grounding, frequently producing hallucinated or geometrically invalid bounding boxes when tasked with object localization. However, a new framework named VANGUARD (Video Anomaly Understanding through Reasoning and Grounding) aims to significantly enhance VAD capabilities by unifying anomaly classification, spatial grounding, and chain-of-thought reasoning within a single Vision-Language Model (VLM).
The VANGUARD Framework
VANGUARD introduces a comprehensive three-stage curriculum that progressively layers training objectives, which are as follows:
- Classifier Warmup: The process begins with a classifier warmup on frozen backbone features to stabilize the model’s initial learning phase.
- LoRA-adapted Spatial Grounding: Following the warmup, the model adapts low-rank adaptation (LoRA) techniques to refine spatial grounding capabilities.
- Chain-of-Thought Generation: The final stage involves generating chain-of-thought reasoning, allowing the model to articulate its decision-making process.
This innovative approach addresses the sparse annotations commonly found in VAD benchmarks. VANGUARD employs a teacher-student annotation pipeline where the VLM, specifically Qwen3-VL-4B, generates structured per-subclip reasoning trajectories based on the manual annotations available from the UCA Dataset. This method enhances the model’s understanding of complex video scenarios.
Performance and Results
In a series of experiments conducted on the UCF-Crime dataset, VANGUARD achieved impressive results, posting a 94% ROC-AUC score alongside an 84% F1 score. Remarkably, it also produced interpretable chain-of-thought explanations and spatial grounding of anomalous objects—capabilities that have been notably absent from prior VAD methods. The unique structure of VANGUARD’s training not only outperformed traditional monolithic optimization techniques but also demonstrated that structured reasoning serves as an implicit regularizer, yielding more balanced predictions compared to classification-only fine-tuning.
Cross-Domain Generalization
Another significant advantage of VANGUARD is its ability to generalize across different domains without requiring target-domain adaptation. Zero-shot transfer tests conducted on the XD-Violence and ShanghaiTech datasets showcased the model’s robustness and adaptability. This capability is critical for practical applications, as it allows VAD systems to function effectively in diverse environments without extensive retraining.
Conclusion
The introduction of VANGUARD marks a notable advancement in the field of Video Anomaly Detection by combining multimodal large language models with structured reasoning and spatial grounding. This framework not only enhances the precision and interpretability of anomaly detection but also sets a new standard for future research and applications in the realm of computer vision.
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