Nov 9 - Agentic Labeling for Specialized Visual Data

Date, Time and Location Nov 09, 2026 9:00 AM - 10:00 AM PST Online. Register for the Zoom! Frontier multimodal models handle familiar concepts well and struggle on niche ones: sorting drill-core samples into 35 geological classes, or telling adhesion from scarring on a ceramic substrate. Our study across 11 specialized visual datasets in manufacturing inspection, geology, and medical imaging found that a few labeled examples in the prompt can close much of that gap, and it also found where that stops working. What you’ll learn: Niche versus hard: how in-context examples supply domain knowledge a model is missing, and how to tell a niche task from a genuinely hard one What the study measured: on an industrial defect task, one labeled example per class raised macro-F1 from 34% to 59% with no fine-tuning, while on other tasks trained specialists pulled ahead at 5 to 10 examples per class Building a few-shot labeling task: define classes, pick reference examples, preview predictions on a small batch, and refine the instructions Finding the crossover point: when to stop prompting a foundation model and train a specialist on bootstrapped labels Running it on your own data: attendees get free GPU compute from Nebius to try the workflow after the session