Getting video models to learn better, faster

Image and video model performance gains stem from three data-centric strategies: filtering and rebalancing noisy or over-represented samples, enriching annotations with detailed captions, bounding boxes, and font information, and generating synthetic data via finetuned generative ensembles for scarce concepts. The author describes a low-cost CPU-only pipeline that uses PySceneDetect to segment videos at shot boundaries and manual ontology creation to guide heuristic tuning and dataset rebalancing.