VADER: Towards Causal Video Anomaly Understanding with Relation-Aware
  Large Language Models
Authors: name: Ying Cheng, name: Yu-Ho Lin, name: Min-Hung Chen, name: Fu-En Yang, name: Shang-Hong Lai
Organization: NVIDIA

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.