@inproceedings{ditchfield-ogle-mitkov-2025-comparative,
    title = "A Comparative Study of Vision Transformers and Multimodal Language Models for Violence Detection in Videos",
    author = "Ditchfield-Ogle, Tomas  and
      Mitkov, Ruslan",
    editor = "Picazo-Izquierdo, Alicia  and
      Estevanell-Valladares, Ernesto Luis  and
      Mitkov, Ruslan  and
      Guillena, Rafael Mu{\~n}oz  and
      Cerd{\'a}, Ra{\'u}l Garc{\'i}a",
    booktitle = "Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.r2lm-1.2/",
    pages = "10--20",
    abstract = "This project compares methods for de- tecting violent videos, which are crucial for ensuring real-time safety in surveil- lance and digital moderation. It evaluates four approaches: a random forest classi- fier, a transformer model, and two multi- modal vision-language models. The pro- cess involves preprocessing datasets, train- ing models, and assessing accuracy, inter- pretability, scalability, and real-time suit- ability. Results show that traditional meth- ods are simple but less effective. The trans- former model achieved high accuracy, and the multimodal models offered high vio- lence recall with descriptive justifications. The study highlights trade-offs and pro- vides practical insights for the deployment of automated violence detection."
}