Frame selection is the whole game: notes on making LLMs watch video
This article explores the technical challenges of video processing for Large Language Models, specifically focusing on the importance of frame selection over uniform sampling. The author argues that intelligent frame extraction is essential for models to accurately interpret visual data rather than relying on human-curated summaries.
A vision LLM can realistically afford about 150 images per video. Which 150 you pick decides whether the model watched the video or just a slideshow about it. These are my notes from getting this wrong over and over while building claude-real-video , an MIT-licensed local pipeline.
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