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

影片異常理解（VAU）旨在對影片中的異常事件提供細緻闡釋與語義解析，突破傳統方法僅專注於異常檢測與定位的局限性。然而現有方法往往忽略物體間深層因果關係與互動機制，而這些要素對於理解異常行為至關重要。本文提出VADER——一種基於大語言模型的影片異常理解框架，透過整合關鍵幀物體關係特徵與視覺線索，強化從影片中理解異常事件的能力。具體而言，VADER首先運用異常評分器計算逐幀異常分數，再透過情境感知採樣策略捕捉每個異常事件的因果上下文。透過關係特徵提取器與對比關係編碼器的協同工作，動態建模物體互動關係，生成緊湊的關係表徵以供下游推理。這些視覺與關係線索與大語言模型整合後，能生成具因果依據的詳細描述，並支援穩健的異常問答任務。在多個真實場景VAU基準測試上的實驗表明，VADER在異常描述、解釋與因果推理任務中均取得優異表現，推動可解釋影片異常分析的研究前沿。

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

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