@inproceedings{ratul-etal-2025-cuet,
    title = "{CUET}{\_}{N}etwork{S}ociety@{D}ravidian{L}ang{T}ech 2025: A Multimodal Framework to Detect Misogyny Meme in {D}ravidian Languages",
    author = "Ratul, MD Musa Kalimullah  and
      Aftahee, Sabik  and
      Babu, Tofayel Ahmmed  and
      Hossain, Jawad  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/landing_page/2025.dravidianlangtech-1.92/",
    pages = "529--535",
    ISBN = "979-8-89176-228-2",
    abstract = "Memes are commonly used for communication on social media platforms, and some of them can propagate misogynistic content, spreading harmful messages. Detecting such misogynistic memes has become a significant challenge, especially for low-resource languages like Tamil and Malayalam, due to their complex linguistic structures. To tackle this issue, a shared task on detecting misogynistic memes was organized at DravidianLangTech@NAACL 2025. This paper proposes a multimodal deep learning approach for detecting misogynistic memes in Tamil and Malayalam. The proposed model combines fine-tuned ResNet18 for visual feature extraction and indicBERT for analyzing textual content. The fused model was applied to the test dataset, achieving macro F1 scores of 76.32{\%} for Tamil and 80.35{\%} for Malayalam. Our approach led to 7th and 12th positions for Tamil and Malayalam, respectively."
}