H. Fathallah

Language-driven models for video anomaly detection: taxonomy, datasets, and future horizons

This work proposes a novel taxonomy categorizing 34 existing language-driven VAD methods based on learning paradigm, core architecture, adaptation strategy, and functional output, and serves as a foundational resource for the VAD community, advancing the understanding and application of language-driven models in addressing complex VAD tasks.