Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor

HumorDB is a novel image dataset introduced in the paper to investigate visual humor understanding in computer vision. The dataset consists of 3,545 image pairs with varying humor ratings, designed to challenge models in understanding subtle humor and context. Traditional vision-only models struggle, while vision-language models, particularly those incorporating large language models like LLaVA and GPT-4, exhibit better performance. The study evaluates models on tasks such as binary classification, humor ranking, and image comparison, with a focus on pretraining and the role of multimodal inputs. Human evaluations, involving 550 participants, ensure dataset reliability and highlight the importance of originality in humor perception. The dataset, released under CC BY 4.0, serves as a benchmark for humor detection and abstract concept comprehension in AI systems, with implications for content moderation and future research in the field.