Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
<|reference_start|>Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning: Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only capture the coarse-grained semantics and are unable to effectively handle tasks related to comprehension or localization of specific video segments. In light of these challenges, we propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. To support the training of Momentor, we design an automatic data generation engine to construct Moment-10M, a large-scale video instruction dataset with segment-level instruction data. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization. Zero-shot evaluations on several tasks demonstrate that Momentor excels in fine-grained temporally grounded comprehension and localization.<|reference_end|>
@article{qian2024momentor:,
    title={Momentor: Advancing Video Large Language Model with Fine-Grained
  Temporal Reasoning},
    author={Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua,
  Yueting Zhuang, Siliang Tang},
    journal={arXiv preprint arXiv:2402.11435},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11435},
    primaryClass={cs.CV}
}