Movie101v2: Improved Movie Narration Benchmark
<|reference_start|>Movie101v2: Improved Movie Narration Benchmark: Automatic movie narration targets at creating video-aligned plot descriptions to assist visually impaired audiences. It differs from standard video captioning in that it requires not only describing key visual details but also inferring the plots developed across multiple movie shots, thus posing unique and ongoing challenges. To advance the development of automatic movie narrating systems, we first revisit the limitations of existing datasets and develop a large-scale, bilingual movie narration dataset, Movie101v2. Second, taking into account the essential difficulties in achieving applicable movie narration, we break the long-term goal into three progressive stages and tentatively focus on the initial stages featuring understanding within individual clips. We also introduce a new narration assessment to align with our staged task goals. Third, using our new dataset, we baseline several leading large vision-language models, including GPT-4V, and conduct in-depth investigations into the challenges current models face for movie narration generation. Our findings reveal that achieving applicable movie narration generation is a fascinating goal that requires thorough research.<|reference_end|>
@article{yue2024movie101v2:,
    title={Movie101v2: Improved Movie Narration Benchmark},
    author={Zihao Yue, Yepeng Zhang, Ziheng Wang, Qin Jin},
    journal={arXiv preprint arXiv:2404.13370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13370},
    primaryClass={cs.CV cs.CL cs.MM}
}