Dll5143@DravidianLangTech 2025: Majority Voting-Based Framework for Misogyny Meme Detection in Tamil and Malayalam

AbstractMisogyny 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.