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
Most implemented papers
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text
Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U. S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset.
KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes
cozek/memotion2020-code
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SEMEVAL 2020
This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec).
Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes
This work presents Vilio, an implementation of state-of-the-art visio-linguistic models and their application to the Hateful Memes Dataset.
Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge
Memes on the Internet are often harmless and sometimes amusing.
IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada
nikhil6041/OLI-and-Meme-Classification
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This paper describes the IIITK team’s submissions to the offensive language identification, and troll memes classification shared tasks for Dravidian languages at DravidianLangTech 2021 workshop@EACL 2021.
UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention
SiddhanthHegde/You-Need-to-Pay-More-Attention
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EACL (DravidianLangTech) 2021
We propose an ingenious model comprising of a transformer-transformer architecture that tries to attain state-of-the-art by using attention as its main component.
Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification
Our work illustrates different textual analysis methods and contrasting multimodal methods ranging from simple merging to cross attention to utilising both worlds' - best visual and textual features.
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.
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.
