@inproceedings{chen-etal-2025-vqaguider,
    title = "{VQAG}uider: Guiding Multimodal Large Language Models to Answer Complex Video Questions",
    author = "Chen, Yuyan  and
      Jia, Jiyuan  and
      Lu, Jiaxin  and
      Li, Siyue  and
      Guan, Yu  and
      Yang, Ming  and
      Guo, Qingpei",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.385/",
    pages = "7821--7834",
    ISBN = "979-8-89176-251-0",
    abstract = "Complex video question-answering (VQA) requires in-depth understanding of video contents including object and action recognition as well as video classification and summarization, which exhibits great potential in emerging applications in education and entertainment, etc. Multimodal large language models (MLLMs) may accomplish this task by grasping the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. To tackle this task, we first collect a new dedicated Complex VQA dataset named CVQA and then propose VQAGuider, an innovative framework planning a few atomic visual recognition tools by video-related API matching. VQAGuider facilitates a deep engagement with video content and precise responses to complex video-related questions by MLLMs, which is beyond aligning visual and language features for simple VQA tasks. Our experiments demonstrate VQAGuider is capable of navigating the complex VQA tasks by MLLMs and improves the accuracy by 29.6{\%} and 17.2{\%} on CVQA and the existing VQA datasets, respectively, highlighting its potential in advancing MLLMs{'}s capabilities in video understanding."
}