We Are Humor Beings: Understanding and Predicting Visual Humor

TL;DR

This paper investigates visual humor by analyzing abstract scenes, creating datasets, and developing models to predict and modify scene funniness, advancing understanding of humor for AI systems.

Contribution

It introduces new datasets of abstract scenes for humor analysis and proposes models for predicting and altering scene funniness, a novel step in computational humor understanding.

Findings

Models accurately predict scene funniness.

Humans agree with model predictions in qualitative assessments.

Datasets are publicly available for further research.

Abstract

Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor yet. As interactions between humans and AI systems increase, it is imperative that these systems are taught to understand subtleties of human expressions such as humor. In this work, we are interested in the question - what content in a scene causes it to be funny? As a first step towards understanding visual humor, we analyze the humor manifested in abstract scenes and design computational models for them. We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level. We analyze the funny scenes and explore the different types of humor depicted in them via human studies. We model two tasks that we believe demonstrate an understanding of some aspects of visual humor. The tasks involve predicting the funniness of a scene and altering the funniness of a scene. We show that our models perform well quantitatively, and qualitatively through human studies. Our datasets are publicly available.