  Towards open-ended Video Anomaly Detection (VAD), existing methods often
exhibit biased detection when faced with challenging or unseen events and lack
interpretability. To address these drawbacks, we propose Holmes-VAD, a novel
framework that leverages precise temporal supervision and rich multimodal
instructions to enable accurate anomaly localization and comprehensive
explanations. Firstly, towards unbiased and explainable VAD system, we
construct the first large-scale multimodal VAD instruction-tuning benchmark,
i.e., VAD-Instruct50k. This dataset is created using a carefully designed
semi-automatic labeling paradigm. Efficient single-frame annotations are
applied to the collected untrimmed videos, which are then synthesized into
high-quality analyses of both abnormal and normal video clips using a robust
off-the-shelf video captioner and a large language model (LLM). Building upon
the VAD-Instruct50k dataset, we develop a customized solution for interpretable
video anomaly detection. We train a lightweight temporal sampler to select
frames with high anomaly response and fine-tune a multimodal large language
model (LLM) to generate explanatory content. Extensive experimental results
validate the generality and interpretability of the proposed Holmes-VAD,
establishing it as a novel interpretable technique for real-world video anomaly
analysis. To support the community, our benchmark and model will be publicly
available at https://holmesvad.github.io.
