HumorDB: Can AI understand graphical humor?
Vedaant Jain, Felipe dos Santos Alves Feitosa, Gabriel Kreiman

TL;DR
HumorDB is a new dataset designed to evaluate AI's ability to understand visual humor across diverse image types, revealing current models' limitations in capturing subtle humor cues and the need for more sophisticated architectures.
Contribution
The paper introduces HumorDB, a curated dataset for visual humor understanding, and provides a comprehensive evaluation of AI models, highlighting gaps and challenges in current approaches.
Findings
Pretrained vision-language models outperform vision-only models.
Models struggle with abstract sketches and subtle humor cues.
Attention maps show models often focus on incorrect regions.
Abstract
Despite significant advancements in image segmentation and object detection, understanding complex scenes remains a significant challenge. Here, we focus on graphical humor as a paradigmatic example of image interpretation that requires elucidating the interaction of different scene elements in the context of prior cognitive knowledge. This paper introduces \textbf{HumorDB}, a novel, controlled, and carefully curated dataset designed to evaluate and advance visual humor understanding by AI systems. The dataset comprises diverse images spanning photos, cartoons, sketches, and AI-generated content, including minimally contrastive pairs where subtle edits differentiate between humorous and non-humorous versions. We evaluate humans, state-of-the-art vision models, and large vision-language models on three tasks: binary humor classification, funniness rating prediction, and pairwise humor…
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Taxonomy
TopicsHumor Studies and Applications
