@inproceedings{sayma-etal-2025-cuet,
    title = "{CUET}{\_}{N}ovice@{D}ravidian{L}ang{T}ech 2025: A Multimodal Transformer-Based Approach for Detecting Misogynistic Memes in {M}alayalam Language",
    author = "Sayma, Khadiza Sultana  and
      Tofa, Farjana Alam  and
      Osama, Md  and
      Dey, Ashim",
    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/Ingest-2025-COMPUTEL/2025.dravidianlangtech-1.83/",
    pages = "472--477",
    ISBN = "979-8-89176-228-2",
    abstract = "Memes, combining images and text, are a popular social media medium that can spread humor or harmful content, including misogyny{---}hatred or discrimination against women. Detecting misogynistic memes in Malayalam is challenging due to their multimodal nature, requiring analysis of both visual and textual elements. A Shared Task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, aimed to address this issue by promoting the advancement of multimodal machine learning models for classifying Malayalam memes as misogynistic or non-misogynistic. In this work, we explored visual, textual, and multimodal approaches for meme classification. CNN, ResNet50, Vision Transformer (ViT), and Swin Transformer were used for visual feature extraction, while mBERT, IndicBERT, and MalayalamBERT were employed for textual analysis. Additionally, we experimented with multimodal fusion models, including IndicBERT+ViT, MalayalamBERT+ViT, and MalayalamBERT+Swin. Among these, our MalayalamBERT+Swin Transformer model performed best, achieving the highest weighted F1-score of 0.87631, securing 1st place in the competition. Our results highlight the effectiveness of multimodal learning in detecting misogynistic Malayalam memes and the need for robust AI models in low-resource languages."
}