MATK: The Meme Analytical Tool Kit

Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang, Nirmalendu Prakash, Rui Cao +1 more
12/11/2023
cs.CLcs.CVcs.MM

Official code: Social-AI-Studio/MATK (14 stars, last commit 2024-05-29).

Abstract

The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and cultural issues. As a result, people have become interested in categorizing memes, leading to the development of various datasets and multimodal models that show promising results in this field. However, there is currently a lack of a single library that allows for the reproduction, evaluation, and comparison of these models using fair benchmarks and settings. To fill this gap, we introduce the Meme Analytical Tool Kit (MATK), an open-source toolkit specifically designed to support existing memes datasets and cutting-edge multimodal models. MATK aims to assist researchers and engineers in training and reproducing these multimodal models for meme classification tasks, while also providing analysis techniques to gain insights into their strengths and weaknesses. To access MATK, please visit \url{https://github.com/Social-AI-Studio/MATK}.

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Code Implementations(1)

Official repository for ACM Multimedia'23 paper "MATK: The Meme Analytical Tool Kit"

141Shell, PythonJun 10, 20232 years ago

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Cite this paper

@article{hee2023matk,
  title  = {MATK: The Meme Analytical Tool Kit},
  author = {Ming Shan Hee and Aditi Kumaresan and Nguyen Khoi Hoang and Nirmalendu Prakash and Rui Cao and Roy Ka-Wei Lee},
  year   = {2023},
  eprint = {2312.06094},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.06094v1}
}

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