@inproceedings{kong-etal-2025-tuna,
    title = "{TUNA}: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos",
    author = "Kong, Fanheng  and
      Zhang, Jingyuan  and
      Zhang, Hongzhi  and
      Feng, Shi  and
      Wang, Daling  and
      Yu, Linhao  and
      Ji, Xingguang  and
      Tian, Yu  and
      W., V.  and
      Zhang, Fuzheng",
    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.91/",
    pages = "1810--1839",
    ISBN = "979-8-89176-251-0",
    abstract = "Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing benchmarks for video understanding often treat these properties separately or narrowly focus on specific aspects, overlooking the holistic nature of video content. To address this, we introduce TUNA, a temporal-oriented benchmark for fine-grained understanding on dense dynamic videos, with two complementary tasks: captioning and QA. Our TUNA features diverse video scenarios and dynamics, assisted by interpretable and robust evaluation criteria. We evaluate several leading models on our benchmark, providing fine-grained performance assessments across various dimensions. This evaluation reveals key challenges in video temporal understanding, such as limited action description, inadequate multi-subject understanding, and insensitivity to camera motion, offering valuable insights for improving video understanding models."
}