MemeCap: A Dataset for Captioning and Interpreting Memes

TL;DR

MemeCap introduces a new dataset for meme captioning that includes visual metaphors and background context, revealing current vision-language models' struggles with interpreting complex meme content.

Contribution

The paper presents MemeCap, a novel dataset for meme captioning that incorporates visual metaphors and contextual information, highlighting challenges for existing models.

Findings

State-of-the-art VL models perform worse than humans on meme interpretation.

The dataset enables research on visual metaphor understanding in memes.

Current models struggle with visual metaphors despite success in related tasks.

Abstract

Memes are a widely popular tool for web users to express their thoughts using visual metaphors. Understanding memes requires recognizing and interpreting visual metaphors with respect to the text inside or around the meme, often while employing background knowledge and reasoning abilities. We present the task of meme captioning and release a new dataset, MemeCap. Our dataset contains 6.3K memes along with the title of the post containing the meme, the meme captions, the literal image caption, and the visual metaphors. Despite the recent success of vision and language (VL) models on tasks such as image captioning and visual question answering, our extensive experiments using state-of-the-art VL models show that they still struggle with visual metaphors, and perform substantially worse than humans.