LabelUp: Rapid Image Labeling
DOI:
https://doi.org/10.1609/aaaiss.v2i1.27677Keywords:
Data Labeling, No-code, Artificial Intelligence, Human-Centered Design (HCD), USWDS-compliant Interface, Video Data, Image Data, OCR (Optical Character Recognition), Air-gapped Environment, Transformer Neural Network, Computational Optimization, MetroStar, DevSecOps, Cyber, Cloud, Department Of Defense (DoD), JATIC (Joint AI Test Infrastructure Capability), CDAO (Chief Digital And Artificial Intelligence Office), Operational AI Models, ML (Machine Learning), Object Detection, Transformer Technology, In-context-learning Models, AI, LabelUp, Auto-labeling, Scalability, Segment Anything Model, SegGPT, Workflow, Transparency, Feedback LoopAbstract
MetroStar introduces "LabelUp," a transformative auto-labeling AI solution, custom-built for US government applications. Utilizing advanced transformer technology, LabelUp revolutionizes data labeling, significantly en-hancing operational AI models' efficiency for critical de-fense mechanisms. This innovative system promises over 800% workload reduction, facilitating rapid, precise la-beling with its intuitive low-code interface, featuring so-phisticated in-context-learning models. Compared to tra-ditional methods, LabelUp demonstrates staggering time and cost savings, redefining industry benchmarks. The paper further elucidates risk mitigation strategies, ensur-ing robust security and accuracy. In its prototype stage, LabelUp has shown significant potential, forecasting a breakthrough in image/video labeling processes. The white paper culminates with an invitation for a detailed government review and discussion.
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Published
2024-01-22
How to Cite
Early, J., Kelly, E., & Scearce, J. (2024). LabelUp: Rapid Image Labeling. Proceedings of the AAAI Symposium Series, 2(1), 228–229. https://doi.org/10.1609/aaaiss.v2i1.27677
Issue
Section
Assured and Trustworthy Human-centered AI (ATHAI)
