HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding

Heqing Zou; Tianze Luo; Guiyang Xie; Victor Xiao Jie Zhang; Fengmao Lv; Guangcong Wang; Junyang Chen; Zhuochen Wang; Hansheng Zhang; Huaijian Zhang

arXiv · 2501.01645

HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding

Heqing Zou·Tianze Luo·Guiyang Xie·Victor Xiao Jie Zhang·Fengmao Lv·Guangcong Wang·Junyang Chen·Zhuochen Wang·Hansheng Zhang·Huaijian Zhang

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Abstract

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.

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BibTeX RIS

Heqing Zou, Tianze Luo, Guiyang Xie, Victor Xiao Jie Zhang, Fengmao Lv, Guangcong Wang, Junyang Chen, Zhuochen Wang, Hansheng Zhang, Huaijian Zhang. 2025-05-13. HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding. https://arxiv.org/abs/2501.01645

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