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proprietary model, while using only 32 frames and 7B parameters. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the We introduce Video-MME, the first-ever ll-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video ysis. It is designed to comprehensively ess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. -

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