LATERN：测试时上下文感知的可解释视频异常检测
LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
AI总结：
本文提出LATERN框架，通过上下文感知模块和递归证据聚合模块提升视频异常检测的准确性和解释一致性。
摘要：
本文提出LATERN框架，通过上下文感知模块和递归证据聚合模块提升视频异常检测的准确性和解释一致性。
英文摘要：
Vision-language models (VLMs) have recently emerged as a promising paradigm for video anomaly detection (VAD) due to their strong visual reasoning ability and natural language-based explainability. In this paper, we aim to address a key limitation of such pipelines, which perform segment-level inference independently owing to token constraints and reason without structured temporal context, allowing VLMs to interpret anomalies as deviations from evolving video dynamics rather than producing fragmented predictions and explanations. To specify, we propose a context-aware framework named LATERN, which reformulates VAD as a temporal evidence aggregation process. LATERN consists of two complementary modules: Context-Aware Anomaly Scoring (CEA) and Recursive Evidence Aggregation (REA). CEA introduces a novel image-grounded memory mechanism, which selectively chooses historical content via frame diversity and visual-textual alignment as expanded context to help generate reliable anomaly scores. Building upon these scores, REA performs recursive temporal aggregation to identify coherent anomaly intervals and produce event-level decisions and explanations grounded in visual-textual evidence. Extensive experiments on challenging benchmarks, including UCF-Crime and XD-Violence, show that LATERN enhances detection accuracy and explanation consistency for frozen VLMs during test time, while generating temporally coherent and semantically grounded event-level explanations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
