One_by_zero@DravidianLangTech 2025: A Multimodal Approach for Misogyny Meme Detection in Malayalam Leveraging Visual and Textual Features

Misogyny memes are a form of online content that spreads harmful and damaging ideas about women. By combining images and text, they often aim to mock, disrespect, or insult women, sometimes overtly and other times in more subtle, insidious ways. Detecting Misogyny memes is crucial for fostering safer and more respectful online communities. While extensive research has been conducted on high-resource languages (HRLs) like English, low-resource languages (LRLs) such as Dravidian (e.g., Tamil and Malayalam) remain largely overlooked. The shared task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, provided a platform to tackle the challenge of identifying misogynistic content in memes, specifically in Malayalam. We participated in the competition and adopted a multimodal approach to contribute to this effort. For image analysis, we employed a ResNet18 model to extract visual features, while for text analysis, we utilized the IndicBERT model. Our system achieved an impressive F1-score of 0.87, earning us the 3^rd rank in the task.