HumorDB

Fri, 07 Jun 2024 00:00:00 +0000

This work introduces HumorDB, a novel dataset designed to advance visual humor understanding in AI systems. The dataset consists of carefully curated image pairs with contrasting humor ratings, emphasizing subtle visual cues that trigger humor while mitigating potential biases.

Key features of HumorDB include:

Evaluation through multiple tasks: binary classification, range regression, and pairwise comparison
Focus on capturing the subjective nature of humor perception
Potential as a zero-shot benchmark for large multimodal models

Our initial experiments reveal that while vision-only models struggle with humor detection, vision-language models, particularly those leveraging large language models, show promising results. This work contributes to pushing the boundaries of AI’s ability to comprehend nuanced human communication, specifically in the domain of visual humor.

Benchmark Datasets | Vedaant Jain

HumorDB