VADER: Towards Causal Video Anomaly Understanding with Relation-Aware
  Large Language Models
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.
6912dcc8a644ba07c499c81a
Ying Cheng
6912dcc8a644ba07c499c81b
Yu-Ho Lin
6912dcc8a644ba07c499c81c
Min-Hung Chen
claimed_verified
2025-11-11T19:43:17.952Z
64ae22dd1aee69ece065cdcd
https://cdn-avatars.huggingface.co/v1/production/uploads/64ae22dd1aee69ece065cdcd/JG7QaHIrr4i2k4uwR4pZK.png
Min-Hung Chen
cmhungsteve
user
cmhungsteve
6912dcc8a644ba07c499c81d
Fu-En Yang
6912dcc8a644ba07c499c81e
Shang-Hong Lai
VADER: Towards Causal Video Anomaly Understanding with Relation-Aware
  Large Language Models
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, an LLM-driven framework, enhances video anomaly understanding by integrating keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering.
Anomaly Scorer Context-AwarE Sampling Relation Feature Extractor COntrastive Relation Encoder LLMs anomaly description explanation causal reasoning