Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor
| Autor: | Jain, Veedant, Feitosa, Felipe dos Santos Alves, Kreiman, Gabriel |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 7 main figures, 5 additional appendix figures |
| Databáze: | arXiv |
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