MATK: The Meme Analytical Tool Kit

• Main Authors: HEE, Ming Shan, KUMARESAN, Aditi, HOANG, Nguyen Khoi, PRAKASH, Nirmalendu, CAO, Rui, LEE, Roy Ka-Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: meme; multimodal analysis; visual-language models; Graphic Communications; Graphics and Human Computer Interfaces; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8484; https://ink.library.smu.edu.sg/context/sis_research/article/9487/viewcontent/MATK_pv.pdf
• Institution: Singapore Management University

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 https://github.com/Social-AI-Studio/MATK.