Meme Classification

9 papers with code • 1 benchmarks • 1 datasets

Meme classification refers to the task of classifying internet memes.


Use these libraries to find Meme Classification models and implementations

Most implemented papers

Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP 26 Feb 2021

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

bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text LREC 2020

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 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

Muennighoff/vilio 14 Dec 2020

This work presents Vilio, an implementation of state-of-the-art visio-linguistic models and their application to the Hateful Memes Dataset.

IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada

nikhil6041/OLI-and-Meme-Classification 17 Apr 2021

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 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

adeeph/memeclassification 9 Aug 2021

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

facebookresearch/vissl 16 Feb 2022

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.