  Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume
that at least few task-relevant target domain training data are available for
adaptation from the source to the target domain. However, this requires
laborious model-tuning by the end-user who may prefer to have a system that
works ``out-of-the-box." To address such practical scenarios, we identify a
novel target domain (inference-time) VAD task where no target domain training
data are available. To this end, we propose a new `Zero-shot Cross-domain Video
Anomaly Detection (zxvad)' framework that includes a future-frame prediction
generative model setup. Different from prior future-frame prediction models,
our model uses a novel Normalcy Classifier module to learn the features of
normal event videos by learning how such features are different ``relatively"
to features in pseudo-abnormal examples. A novel Untrained Convolutional Neural
Network based Anomaly Synthesis module crafts these pseudo-abnormal examples by
adding foreign objects in normal video frames with no extra training cost. With
our novel relative normalcy feature learning strategy, zxvad generalizes and
learns to distinguish between normal and abnormal frames in a new target domain
without adaptation during inference. Through evaluations on common datasets, we
show that zxvad outperforms the state-of-the-art (SOTA), regardless of whether
task-relevant (i.e., VAD) source training data are available or not. Lastly,
zxvad also beats the SOTA methods in inference-time efficiency metrics
including the model size, total parameters, GPU energy consumption, and GMACs.
