@inproceedings{shi-etal-2026-v,
    title = "v-{HUB}: A Benchmark for Video Humor Understanding from Vision and Sound",
    author = "Shi, Zhengpeng  and
      Zhao, Yanpeng  and
      Zhou, Jianqun  and
      Wang, Yuxuan  and
      Cui, Qinrong  and
      Bi, Wei  and
      Zhu, Song-Chun  and
      Zhao, Bo  and
      Zheng, Zilong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1785/",
    pages = "38544--38567",
    ISBN = "979-8-89176-390-6",
    abstract = "AI models capable of comprehending humor hold real-world promise{---}for example, enhancing engagement in human-machine interactions. To gauge and diagnose the capacity of multimodal large language models (MLLMs) for humor understanding, we introduce v-HUB, a novel video humor understanding benchmark. v-HUB comprises a curated collection of non-verbal short videos, reflecting real-world scenarios where humor can be appreciated purely through visual cues. We pair each video clip with rich annotations to support a variety of evaluation tasks and analyses, including a novel study of environmental sound that can enhance humor. To broaden its applicability, we construct an open-ended QA task, making v-HUB readily integrable into existing video understanding task suites. We evaluate a diverse set of MLLMs, from specialized Video-LLMs to versatile OmniLLMs that can natively process audio, covering both open-source and proprietary domains. The experimental results expose the difficulties MLLMs face in comprehending humor from visual cues alone. Our findings also demonstrate that incorporating audio helps with video humor understanding, highlighting the promise of integrating richer modalities for complex video understanding tasks."
}