I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes
In this paper we present our approach and system description on Task 5 A in MAMI: Multimedia Automatic Misogyny Identification. In our experiments we compared several architectures based on deep learning algorithms with various other approaches to binary classification using Transformers, combined with a nudity image detection algorithm to provide better results. With this approach, we achieved an F1-score of 0.665 in the evaluation process
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LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining
In current times, memes have become one of the most popular mediums to share jokes and information with the masses over the internet. Memes can also be used as tools to spread hatred and target women through degrading co…
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This paper describes INF-UFRGS submission for SemEval-2022 Task 5 Multimodal Automatic Misogyny Identification (MAMI). Unprecedented levels of harassment came with the ever-growing internet usage as a mean of worldwide c…
IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers
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Mitra Behzadi at SemEval-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on CLIP
Everyday more users are using memes on social media platforms to convey a message with text and image combined. Although there are many fun and harmless memes being created and posted, there are also ones that are hatefu…
HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost…