Abstract: A video anomaly detection (VAD) system may have an induction stage receiving a plurality of video frames as a reference and deriving a rule for a normal event occurrence and a corresponding rule for an anomaly event occurrence by contrasting the corresponding rule for the anomaly event occurrence to the rule for the normal event occurrence. The VAD system may have a deduction stage applying the rule for the normal event occurrence and the corresponding rule for the anomaly event occurrence to determine anomalies in non-reference video frames.

Abstract: Systems and methods for augmenting large video language models (LVLMs) by incorporation of a plausible action anticipation framework. The framework augments the LVLM by taking into account the aspect of plausibility in an action sequence. Two objective functions directed to counterfactual-based plausible action sequence learning loss and a long-horizon action repetition loss are derived and used to train the LVLM to generate plausible anticipated action sequences. The augmented LVLM is then able to produce sequences whereby each action in the sequence is temporally and spatially factual with respect to the others.