Major brands make multi-million dollar ad decisions with a tool that predates the iPhone 4, has never been updated, and was never designed for video.
Unlike competitors who report one inflated number, IRIS is validated across five independent saliency metrics — each designed to catch a different failure mode.
A deep learning pipeline trained end-to-end on real human gaze data — with full video support no competitor offers.
All 3M figures sourced directly from their own 2010 validation study. We're not making claims — we're quoting their own published results.
| Metric | 3M VAS — their own study | IRIS |
|---|---|---|
| AUC on advertisements | 0.73 | 0.995 +36% |
| Predictive efficiency (ads) | 79% | 96.5% |
| Pearson correlation | Not reported | 0.981 |
| KL divergence | Not reported | 0.017 |
| Video ad support | None | Frame-by-frame + audio Industry first |
| Architecture | Rule-based (2010) | Deep learning (ResNet50) |
| Training data | Generic scenes | Ad-specific eye-tracking |
| Last updated | Never | Active development |
| Metrics reported | 1 (ROC only) | 5 (AUC, CC, KL, NSS, MAE) |
* Source: 3M Visual Attention Service Validation Study, 3M Commercial Graphics Division, 2010.
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IRIS was trained on real eye-tracking data from human participants viewing actual advertising content — not generic stock images or synthetic data.
Whether you're an agency, brand, or researcher — reach out to discuss how IRIS can replace your current attention testing workflow.