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a new state-of-the-art accuracy of 35.8%, surping GPT-4o, a 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 Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ? on GitHub for latest update. ? I also have other video-language projects that may interest you . Open-Sora Plan: Open-Source Large Video Generation Model Check the YouTube

capabilities. Video Overviews, including voices and visuals, are AI-generated and may contain inaccuracies or audio glitches. NotebookLM may take a while to generate the Video Overview, feel free to come back to your notebook later. Introduced a novel taxonomy for Vid-LLMs based on video representation and LLM nctionality. Added a Preliminary chapter, reclifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the LLM Background section.

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