Momentor: Advancing Video Large Language Model With Fine-grained Temporal Reasoning

Qian Long, Li Juncheng, Wu Yu, Ye Yaobo, Fei Hao, Chua Tat-seng, Zhuang Yueting, Tang Siliang. Arxiv 2024

[Paper]    
Reinforcement Learning Training Techniques

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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