AI RESEARCH
LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
arXiv CS.CV
•
ArXi:2605.15054v1 Announce Type: new 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.