byteSizedLLM@DravidianLangTech 2025: Multimodal Misogyny Meme Detection in Low-Resource Dravidian Languages Using Transliteration-Aware XLM-RoBERTa, ResNet-50, and Attention-BiLSTM
Durga Prasad Manukonda, Rohith Gowtham Kodali · 2025
Detecting misogyny in memes is challenging due to their multimodal nature, especially in low-resource languages like Tamil and Malayalam.This paper presents our work in the Misogyny Meme Detection task, utilizing both textual and visual features.We propose an Attention-Driven BiLSTM-XLM-RoBERTa-ResNet model, combining a transliterationaware fine-tuned XLM-RoBERTa for text analysis and ResNet-50 for image feature extraction.Our model achieved Macro-F1 scores of 0.8805 for Malayalam and 0.8081 for Tamil, demonstrating competitive performance.However, challenges such as class imbalance and domainspecific image representation persist.Our findings highlight the need for better dataset curation, task-specific fine-tuning, and advanced fusion techniques to enhance multimodal hate speech detection in Dravidian languages.