Industry Applicationscomputer visionannotation workflowsdata scaling
ML@CMU Builds Video Caption Pipeline at Scale
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5.6
ML@CMU built a year-long video caption pipeline with 100+ professional creators, documenting the development process and outcomes. The project taught the team that scaling supervision, rather than scaling models, was the key lesson for improving caption production and workflows.
Scoring Rationale
Practical, hands-on case study with significant relevance for teams building captioning pipelines and data workflows; moderate impact for the broader AI research community.
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