VLMs vs. Specialist Models: When to Prompt and When to Train

Frontier vision-language models can learn many specialized visual classification tasks through in-context learning, where labeled examples are included in prompts rather than used to retrain model weights. Across 11 datasets, adding one labeled example per class improved macro-F1 on 10 tasks, with drill-core rock classification rising from 20% to 73% using two examples per category and ceramic-defect classification improving from 34% to 59% with one example per class. The findings suggest that many zero-shot failures reflect missing task-specific domain context rather than an absence of visual capability, although examples cannot solve tasks whose distinctions require unavailable information or stronger reasoning, as shown by consistently low results on dermatology images. Prompted VLMs are presented as useful for rapidly exploring tasks and bootstrapping labels when data is scarce, while specialist models such as fine-tuned ConvNeXt-T or DINOv2 probes often surpass them once roughly 5–14 labeled images per class are available and production needs justify training, maintenance, latency, or cost tradeoffs. Voxel51’s Agentic Labeling workflow is designed to support this process by allowing users to define labels, provide examples, review predictions, refine prompts, scale labeling, and eventually train specialists from validated data.

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