Despite significant advancements in computer vision, understanding complex
scenes, particularly those involving humor, remains a substantial challenge.
This paper introduces HumorDB, a novel image-only dataset specifically designed
to advance visual humor understanding. HumorDB consists of meticulously curated
image pairs with contrasting humor ratings, emphasizing subtle visual cues that
trigger humor and mitigating potential biases. The dataset enables evaluation
through binary classification(Funny or Not Funny), range regression(funniness
on a scale from 1 to 10), and pairwise comparison tasks(Which Image is
Funnier?), effectively capturing the subjective nature of humor perception.
Initial experiments reveal that while vision-only models struggle,
vision-language models, particularly those leveraging large language models,
show promising results. HumorDB also shows potential as a valuable zero-shot
benchmark for powerful large multimodal models. We open-source both the dataset
and code under the CC BY 4.0 license.

通过 HumorDB 数据集，该研究论文介绍了一种用于提升视觉幽默理解能力的图像数据集，着重于触发幽默的微妙视觉线索，并缓解潜在的偏差。该数据集可以通过二元分类（有趣或无趣）、范围回归（将幽默性评分从 1 到 10 进行量化）和成对比较任务（哪个图像更有趣？）进行评估，有效捕捉了幽默感知的主观性。初步实验表明，虽然纯视觉模型面临困难，但视觉语言模型，特别是利用大型语言模型的模型，表现出有希望的结果。HumorDB 还展示了作为功能强大的大型多模态模型的一个有价值的零样本基准的潜力。我们在 CC BY 4.0 许可证下开源了数据集和代码。