TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes
We describe our system for the SemEval 2022 task on detecting misogynous content in memes. This is a pressing problem and we explore various methods ranging from traditional machine learning to deep learning models such as multimodal transformers. We propose a multimodal BERT architecture that uses information from both image and text. We further incorporate common world knowledge from pretrained CLIP and Urban dictionary. We also provide qualitative analysis to support out model. Our best performing model achieves an F1 score of 0.679 on Task A (Rank 5) and 0.680 on Task B (Rank 13) of the hidden test set. Our code is available at https://github.com/paridhimaheshwari2708/MAMI.
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Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes
Misogynistic memes are rampant on social media, and often convey their messages using multimodal signals (e.g., images paired with derogatory text or captions). However, to date very few multimodal systems have been leve…
Contrastive LearningPoirot at SemEval-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection
In recent years, there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended the boundary of online harassment against women and created an unwa…
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning
We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-tas…
Ensemble LearningMulti-Task LearningIITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers
This paper presents our submission to task 5 ( Multimedia Automatic Misogyny Identification) of the SemEval 2022 competition. The purpose of the task is to identify given memes as misogynistic or not and further label th…
Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content
In this paper we present a benchmark dataset generated as part of a project for automatic identification of misogyny within online content, which focuses in particular on memes. The benchmark here described is composed o…