@inproceedings{mohiuddin-etal-2025-cuet,
    title = "{CUET}-{NLP}{\_}{MP}@{D}ravidian{L}ang{T}ech 2025: A Transformer-Based Approach for Bridging Text and Vision in Misogyny Meme Detection in {D}ravidian Languages",
    author = "Mohiuddin, Md.  and
      Kabir, Md Minhazul  and
      Ahmed, Kawsar  and
      Hoque, Mohammed Moshiul",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.dravidianlangtech-1.90/",
    pages = "514--521",
    ISBN = "979-8-89176-228-2",
    abstract = "Misogyny memes, a form of digital content, reflect societal prejudices by discriminating against women through shaming and stereotyping. In this study, we present a multimodal approach combining Indic-BERT and ViT-base-patch16-224 to address misogyny memes. We explored various Machine Learning, Deep Learning, and Transformer models for unimodal and multimodal classification using provided Tamil and Malayalam meme dataset. Our findings highlight the challenges traditional ML and DL models face in understanding the nuances of Dravidian languages, while emphasizing the importance of transformer models in capturing these complexities. Our multimodal method achieved F1-scores of 77.18{\%} and 84.11{\%} in Tamil and Malayalam, respectively, securing 6th place for both languages among the participants."
}