  The recent developments in Large Multi-modal Video Models (Video-LMMs) have
significantly enhanced our ability to interpret and analyze video data. Despite
their impressive capabilities, current Video-LMMs have not been evaluated for
anomaly detection tasks, which is critical to their deployment in practical
scenarios e.g., towards identifying deepfakes, manipulated video content,
traffic accidents and crimes. In this paper, we introduce VANE-Bench, a
benchmark designed to assess the proficiency of Video-LMMs in detecting and
localizing anomalies and inconsistencies in videos. Our dataset comprises an
array of videos synthetically generated using existing state-of-the-art
text-to-video generation models, encompassing a variety of subtle anomalies and
inconsistencies grouped into five categories: unnatural transformations,
unnatural appearance, pass-through, disappearance and sudden appearance.
Additionally, our benchmark features real-world samples from existing anomaly
detection datasets, focusing on crime-related irregularities, atypical
pedestrian behavior, and unusual events. The task is structured as a visual
question-answering challenge to gauge the models' ability to accurately detect
and localize the anomalies within the videos. We evaluate nine existing
Video-LMMs, both open and closed sources, on this benchmarking task and find
that most of the models encounter difficulties in effectively identifying the
subtle anomalies. In conclusion, our research offers significant insights into
the current capabilities of Video-LMMs in the realm of anomaly detection,
highlighting the importance of our work in evaluating and improving these
models for real-world applications. Our code and data is available at
https://hananshafi.github.io/vane-benchmark/
