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LVBench: An Extreme Long Video Understanding Benchmark

Paper · 2024 · Weihan Wang, Zehai He, Wenyi Hong et al.

Summary

Most video benchmarks for multimodal large language models (MLLMs) test short clips or single images, so they say little about whether a model can follow events over hours. LVBench fills this gap. The authors started from about 500 YouTube videos in six genres: sports, documentaries, event records, lifestyle, TV/drama and cartoons. Annotators filtered these by hand for:

  • a clear protagonist,
  • narrative coherence,
  • event density,
  • visual quality,
  • answerability from visuals alone.

The final set is 103 videos, each at least 30 minutes long. Human experts wrote multiple-choice questions around six skills that can be combined: temporal grounding, summarization, reasoning, entity recognition, event understanding and key information retrieval. Strong current models, including Gemini-2.5-Pro, Seed1.5-VL and Qwen2.5-VL-72B, remain well below human accuracy. Accuracy rises sharply when frames are sampled densely rather than sparsely. Without any frames, models score near chance, which indicates the questions cannot be solved from text alone. The benchmark gives a demanding testbed for long-term memory and temporal comprehension in video models.

Key points

  • Scale: 103 videos (117 hours) and 1,549 manually annotated QA pairs. Average video length is 4,101 seconds, roughly four times that of earlier long-video sets such as Video-MME, MoVQA and LongVideoBench.
  • Six skill categories: Temporal Grounding (TG), Summarization (Sum), Reasoning, Entity Recognition (ER), Event Understanding (EU) and Key Information Retrieval (KIR). ER and EU make up the largest share of questions.
  • Frame ablation (0, 1, 4, 8 and 50 frames, plus 1 FPS):
    • Going from 1 to 8 frames gives small gains.
    • Moving from 50 frames to 1 FPS gives the largest jump.
    • With 0 frames, results are close to random guessing.
  • Audio is deliberately excluded, which the authors acknowledge as a limitation.
  • The data is released under CC-BY-NC-SA-4.0. Only YouTube links are distributed; stored copies are available if the original creators give permission.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tsinghua UniversityBeijing · China415 works · 94,028 citations🏢Zhipu AIBeijing · China24 works · 2,453 citations

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1,341 works

Cite

Cite as Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Xiaotao Gu, Shiyu Huang, Bin Xu, Yuxiao Dong, Ming Ding and Jie Tang. 2024. LVBench: An Extreme Long Video Understanding Benchmark. arXiv:2406.08035.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2406.08035 · arXiv 2406.08035

@misc{wang2024lvbench,
  title = {{LVBench: An Extreme Long Video Understanding Benchmark}},
  author = {Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Xiaotao Gu, Shiyu Huang, Bin Xu, Yuxiao Dong, Ming Ding and Jie Tang},
  year = {2024},
  doi = {10.48550/arxiv.2406.08035},
  eprint = {2406.08035},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2406.08035}
}

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