VADER：基于关系感知大语言模型的因果视频异常理解研究

摘要

视频异常理解（VAU）旨在对视频中的异常事件提供精细化解析与语义层面的认知，突破了传统方法仅关注异常检测与定位的局限性。然而现有研究往往忽略对异常行为理解至关重要的深层因果关系与物体间交互。本文提出VADER——一种基于大语言模型的视频异常理解框架，通过融合关键帧物体关联特征与视觉线索来增强视频异常认知能力。具体而言，VADER首先采用异常评分器计算逐帧异常分数，继而通过上下文感知采样（CAES）策略捕捉每个异常事件的因果上下文。通过关系特征提取器与对比式关系编码器（CORE）协同建模动态物体交互，生成紧凑的关系表征以供下游推理。这些视觉与关系线索与大语言模型集成，可生成具有因果依据的详细描述，并支持稳健的异常相关问答。在多个真实场景VAU基准测试上的实验表明，VADER在异常描述、解释和因果推理任务中均取得优异结果，推动了可解释视频异常分析的前沿发展。

English

Video anomaly understanding (VAU) aims to provide detailed interpretation and semantic comprehension of anomalous events within videos, addressing limitations of traditional methods that focus solely on detecting and localizing anomalies. However, existing approaches often neglect the deeper causal relationships and interactions between objects, which are critical for understanding anomalous behaviors. In this paper, we propose VADER, an LLM-driven framework for Video Anomaly unDErstanding, which integrates keyframe object Relation features with visual cues to enhance anomaly comprehension from video. Specifically, VADER first applies an Anomaly Scorer to assign per-frame anomaly scores, followed by a Context-AwarE Sampling (CAES) strategy to capture the causal context of each anomalous event. A Relation Feature Extractor and a COntrastive Relation Encoder (CORE) jointly model dynamic object interactions, producing compact relational representations for downstream reasoning. These visual and relational cues are integrated with LLMs to generate detailed, causally grounded descriptions and support robust anomaly-related question answering. Experiments on multiple real-world VAU benchmarks demonstrate that VADER achieves strong results across anomaly description, explanation, and causal reasoning tasks, advancing the frontier of explainable video anomaly analysis.

VADER：基于关系感知大语言模型的因果视频异常理解研究

VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models

摘要

Support