MATK: The Meme Analytical Tool Kit
This toolkit addresses a gap for researchers and engineers working on meme analysis by providing a standardized framework, though it is incremental as it builds on existing datasets and models.
The authors tackled the lack of a unified library for reproducing and evaluating multimodal models in meme classification by introducing MATK, an open-source toolkit that supports existing datasets and models, enabling researchers to train, compare, and analyze these models effectively.
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}.