Multimodal large language models have become a popular topic in deep visual understanding due to many promising real-world applications. However, hour-long video understanding, spanning over one hour and containing tens of thousands of visual frames, remains under-explored because of 1) challenging long-term video analyses, 2) inefficient large-model approaches, and 3) lack of large-scale benchmark datasets. Among them, in this paper, we focus on building a large-scale hour-long long video benchmark, HLV-1K, designed to evaluate long video understanding models. HLV-1K comprises 1009 hour-long videos with 14,847 high-quality question answering (QA) and multi-choice question asnwering (MCQA) pairs with time-aware query and diverse annotations, covering frame-level, within-event-level, cross-event-level, and long-term reasoning tasks. We evaluate our benchmark using existing state-of-the-art methods and demonstrate its value for testing deep long video understanding capabilities at different levels and for various tasks. This includes promoting future long video understanding tasks at a granular level, such as deep understanding of long live videos, meeting recordings, and movies.
@article{arxiv.2501.01645,
title = {HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding},
author = {Heqing Zou and Tianze Luo and Guiyang Xie and Victor Xiao Jie Zhang and Fengmao Lv and Guangcong Wang and Junyang Chen and Zhuochen Wang and Hansheng Zhang and Huaijian Zhang},
journal= {arXiv preprint arXiv:2501.01645},
year = {2025}
}