@inproceedings{chowdhury-etal-2025-cuet,
title = "{CUET}{\_}320@{LT}-{EDI}-2025: A Multimodal Approach for Misogyny Meme Detection in {C}hinese Social Media",
author = "Chowdhury, Madiha Ahmed and
Khan, Lamia Tasnim and
Hasan, Md. Shafiqul and
Dey, Ashim",
editor = "Gkirtzou, Katerina and
{\v{Z}}itnik, Slavko and
Gracia, Jorge and
Gromann, Dagmar and
di Buono, Maria Pia and
Monti, Johanna and
Ionov, Maxim",
booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = sep,
year = "2025",
address = "Naples, Italy",
publisher = "Unior Press",
url = "https://aclanthology.org/2025.ltedi-1.30/",
pages = "184--189",
ISBN = "978-88-6719-334-9",
abstract = "Detecting misogyny in memes is challenging due to their complex interplay of images and text that often disguise offensive content. Current AI models struggle with these cross-modal relationships and contain inherent biases. We tested multiple approaches for the Misogyny Meme Detection task at LT-EDI@LDK 2025: ChineseBERT, mBERT, and XLM-R for text; DenseNet, ResNet, and InceptionV3 for images. Our best-performing system fused fine-tuned ChineseBERT and DenseNet features, concatenating them before final classification through a fully connected network. This multimodal approach achieved a 0.93035 macro F1-score, winning 1st place in the competition and demonstrating the effectiveness of our strategy for analyzing the subtle ways misogyny manifests in visual-textual content."
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<abstract>Detecting misogyny in memes is challenging due to their complex interplay of images and text that often disguise offensive content. Current AI models struggle with these cross-modal relationships and contain inherent biases. We tested multiple approaches for the Misogyny Meme Detection task at LT-EDI@LDK 2025: ChineseBERT, mBERT, and XLM-R for text; DenseNet, ResNet, and InceptionV3 for images. Our best-performing system fused fine-tuned ChineseBERT and DenseNet features, concatenating them before final classification through a fully connected network. This multimodal approach achieved a 0.93035 macro F1-score, winning 1st place in the competition and demonstrating the effectiveness of our strategy for analyzing the subtle ways misogyny manifests in visual-textual content.</abstract>
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%0 Conference Proceedings
%T CUET_320@LT-EDI-2025: A Multimodal Approach for Misogyny Meme Detection in Chinese Social Media
%A Chowdhury, Madiha Ahmed
%A Khan, Lamia Tasnim
%A Hasan, Md. Shafiqul
%A Dey, Ashim
%Y Gkirtzou, Katerina
%Y Žitnik, Slavko
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-334-9
%F chowdhury-etal-2025-cuet
%X Detecting misogyny in memes is challenging due to their complex interplay of images and text that often disguise offensive content. Current AI models struggle with these cross-modal relationships and contain inherent biases. We tested multiple approaches for the Misogyny Meme Detection task at LT-EDI@LDK 2025: ChineseBERT, mBERT, and XLM-R for text; DenseNet, ResNet, and InceptionV3 for images. Our best-performing system fused fine-tuned ChineseBERT and DenseNet features, concatenating them before final classification through a fully connected network. This multimodal approach achieved a 0.93035 macro F1-score, winning 1st place in the competition and demonstrating the effectiveness of our strategy for analyzing the subtle ways misogyny manifests in visual-textual content.
%U https://aclanthology.org/2025.ltedi-1.30/
%P 184-189
Markdown (Informal)
[CUET_320@LT-EDI-2025: A Multimodal Approach for Misogyny Meme Detection in Chinese Social Media](https://aclanthology.org/2025.ltedi-1.30/) (Chowdhury et al., LTEDI 2025)
ACL