GenAI-powered image embeddings propagate annotations from 4–8 seed examples across thousands of similar images - 80% cheaper than traditional pipelines.
Label 4–8 seed images and QpiAI PRO propagates those annotations across every visually similar image in your dataset using GenAI embedding clusters. The result is an 80% reduction in annotation cost - without sacrificing accuracy.
Choose 4–8 representative images from your dataset and apply your labels. QpiAI PRO's interface surfaces visually diverse candidates automatically, so your seed examples cover the full range of variation in your data - not just the easiest cases.
QpiAI PRO generates high-dimensional embedding vectors for every image in your dataset and groups them into clusters of visual similarity. Images that share lighting conditions, object shape, perspective, and context are grouped together - ensuring propagated labels are semantically appropriate.
Annotations from your seed examples are propagated across each cluster automatically. Images that fall outside cluster boundaries or have low similarity scores are flagged for expert review. Your domain specialists only see the true outliers - typically 4–8% of the dataset - while the system handles the rest confidently.
GenAI-powered visual embeddings group images by true semantic similarity - not just pixel proximity - ensuring accurate label propagation across diverse visual conditions.
Pre-tuned embedding models for medical imaging, automotive, and geospatial domains produce clusters that respect domain-specific visual structure - not just general appearance.
A purpose-built review interface surfaces outlier images and low-confidence propagations. Domain experts see only what needs their attention - keeping review sessions focused and efficient.
Statistical anomaly detection identifies images that do not fit cleanly into any cluster, preventing mislabeled edge cases from contaminating your training data before they cause model failures.
Designed for scenarios where labeled data is inherently scarce - rare disease imaging, accident reconstruction, remote sensing - few-shot propagation extracts maximum signal from minimum labels.
96% agreement with expert ground truth - achieved from just 4–8 seed examples per class. The platform gets smarter with each validation cycle, continuously improving cluster quality.
Start your first few-shot annotation project for free. No credit card, no setup, no DevOps required.