@inproceedings{pattanaik-etal-2025-dll5143,
    title = "Dll5143@{D}ravidian{L}ang{T}ech 2025: Majority Voting-Based Framework for Misogyny Meme Detection in {T}amil and {M}alayalam",
    author = "Pattanaik, Sarbajeet  and
      Yadav, Ashok  and
      Singh, Vrijendra",
    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-1.33/",
    pages = "191--199",
    ISBN = "979-8-89176-228-2",
    abstract = "Misogyny memes pose a significant challenge on social networks, particularly in Dravidian-scripted languages, where subtle expressions can propagate harmful narratives against women. This paper presents our approach for the ``Shared Task on MisogynyMeme Detection,'' organized as part of DravidianLangTech@NAACL 2025, focusing on misogyny meme detection in Tamil andMalayalam. To tackle this problem, we proposed a multi-model framework that integrates three distinct models: M1 (ResNet-50 + google/muril-large-cased), M2 (openai/clipvit- base-patch32 + ai4bharat/indic-bert), and M3 (ResNet-50 + ai4bharat/indic-bert). Thefinal classification is determined using a majority voting mechanism, ensuring robustness by leveraging the complementary strengths ofthese models. This approach enhances classification performance by reducing biases and improving generalization. Our model achievedan F1 score of 0.77 for Tamil, significantly improving misogyny detection in the language. For Malayalam, the framework achieved anF1 score of 0.84, demonstrating strong performance. Overall, our method ranked 5th in Tamil and 4th in Malayalam, highlighting itscompetitive effectiveness in misogyny meme detection."
}