Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

• URL: http://arxiv.org/abs/2402.11435v2
• Date: Sun, 2 Jun 2024 05:40:18 GMT
• Title: Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
• Authors: Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua, Yueting Zhuang, Siliang Tang,
• Abstract summary: We propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization.
• Score: 102.54669633984278
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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