Meme Classification
20 papers with code • 2 benchmarks • 4 datasets
Meme classification refers to the task of classifying internet memes.
Benchmarks
These leaderboards are used to track progress in Meme Classification
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Libraries
Use these libraries to find Meme Classification models and implementations
Hateful Memes
HarMeme
Tamil Memes
Latest papers
IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
exploration-lab/iitk-semeval-2024-task-4-pursuasion-techniques
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Memes are one of the most popular types of content used in an online disinformation campaign.
MATK: The Meme Analytical Tool Kit
The rise of social media platforms has brought about a new digital culture called memes.
BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
hate-alert/banglaabusememe
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Finally, we perform a qualitative error analysis of the misclassified memes of the best-performing text-based, image-based and multimodal models.
Mapping Memes to Words for Multimodal Hateful Meme Classification
miccunifi/issues
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Multimodal image-text memes are prevalent on the internet, serving as a unique form of communication that combines visual and textual elements to convey humor, ideas, or emotions.
Decoding the Underlying Meaning of Multimodal Hateful Memes
Recent studies have proposed models that yielded promising performance for the hateful meme classification task.
MemeGraphs: Linking Memes to Knowledge Graphs
In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture.
MemeFier: Dual-stage Modality Fusion for Image Meme Classification
Hate speech is a societal problem that has significantly grown through the Internet.
Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features
A simple classifier based on the FIM representation is able to achieve state-of-the-art performance on the Hateful Memes Challenge (HMC) dataset with an AUROC of 85. 8, which even surpasses the human performance of 82. 65.
MUTE: A Multimodal Dataset for Detecting Hateful Memes
The exponential surge of social media has enabled information propagation at an unprecedented rate.
Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework
ahmed-mahran/mami2022
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SemEval (NAACL) 2022
In this paper we describe our work towards building a generic framework for both multi-modal embedding and multi-label binary classification tasks, while participating in task 5 (Multimedia Automatic Misogyny Identification) of SemEval 2022 competition.
