BANMIME : Misogyny Detection with Metaphor Explanation on Bangla Memes

Detecting misogyny in multimodal content remains a notable challenge, particularly in culturally conservative and low-resource contexts like Bangladesh. While existing research has explored hate speech and general meme classification, the nuanced identification of misogyny in Bangla memes, rich in metaphor, humor, and visual-textual interplay, remains severely underexplored. To address this gap, we introduce BANMIME , the first comprehensive Bangla misogynistic meme dataset comprising 2,000 culturally grounded samples where each meme includes misogyny labels, humor categories, metaphor localization, and detailed human-written explanations. We benchmark the various performances of open and closedsource vision-language models (VLMs) under zero-shot and prompt-based settings and evaluate their capacity for both classification and explanation generation. Furthermore, we systematically explore multiple fine-tuning strategies, including standard, data-augmented, and Chain-of-Thought (CoT) supervision. Our results demonstrate that CoT-based fine-tuning consistently enhances model performance, both in terms of accuracy and in generating meaningful explanations. We envision BANMIME as a foundational resource for advancing explainable multimodal moderation systems in lowresource and culturally sensitive settings. The code and dataset are publicly available at https://github.com/Ayon128/BANMIME . Disclaimer: This paper contains elements that one might find offensive which cannot be avoided due to the nature of the work.