FENG-GUI ALTERNATIVE
The pioneer drew the first maps. The science redrew them.
TL;DR
Feng-GUI has been simulating human attention since around 2007 — a genuine pioneer, built on classic saliency algorithms: contrast, edges, color, handcrafted rules from the pre-deep-learning era. Heatpoints runs UNISAL, a peer-reviewed deep network (ECCV 2020, open source) trained on real eye-tracking datasets — SALICON, MIT1003, CAT2000 — and wraps it in a full workflow: AI audit, Words Map, Fix + re-score, weekly monitoring. Same question, two technological eras.
TWO ERAS OF THE SAME IDEA
> edge_detection ....... OK
> color_contrast ....... OK
> intensity_filters ....... OK
> center_bias ....... OK
> face_heuristics ....... OK
> render: heatmap [watermarked demo]
> _
UNISAL — peer-reviewed at ECCV 2020, open source
Trained on real eye-tracking datasets — faces, text, context.
Then: audit, Words Map, Fix, monitoring.
Feature by feature
| CRITERION | FENG-GUI | HEATPOINTS |
|---|---|---|
| Approach | Classic saliency algorithms — handcrafted rules | Deep network trained on real eye-tracking data |
| Scientific basis | Pre-deep-learning heuristics (~2007 era) | UNISAL — ECCV 2020, peer-reviewed, open source |
| Training data | None — rules, not training | SALICON · MIT1003 · CAT2000 — real gaze datasets |
| Free tier | Watermarked demo | 10 free scans/month + a scanner with no account |
| AI-written audit | — | Included in every report |
| Word-level copy analysis | — | Words Map — unique to Heatpoints |
| Fix loop | — | Fix + re-score in one click |
| Live-page monitoring | — | Weekly re-scans |
| White-label PDF + API | — | Solo, 59 €/mo |
| Tracking on your visitors | None | None — zero script, zero cookie, zero banner |
| Entry price | Paid plans on feng-gui.com | Free 10/mo · Pro 29 € · Solo 59 € · Agency 99 € |
Honest note: Feng-GUI deserves respect — it kept algorithmic attention analysis alive for nearly two decades, long before deep learning made it mainstream. If you want a quick, simple check on print, billboards or packaging, its classic saliency pass still does a job. We compete on the science and what surrounds it: a peer-reviewed model trained on real gaze data, plus the audit, copy analysis, fixes and monitoring your web pages actually need.
FAQ
What is Feng-GUI exactly?
One of the true pioneers of algorithmic attention analysis, active since around 2007. It simulates human vision with classic saliency algorithms — contrast, edges, color intensity, handcrafted rules — and renders attention heatmaps for images and pages. The free demo output is watermarked.
What's the difference between classic saliency and deep-learning saliency?
Classic saliency (Feng-GUI's era) computes attention from handcrafted filters: where contrast is high, where edges meet, where colors pop. Deep-learning saliency — UNISAL, the model behind Heatpoints — is a neural network trained on real eye-tracking datasets (SALICON, MIT1003, CAT2000), so it learned from where humans actually looked: faces, text and context included. It was peer-reviewed at ECCV 2020 and is open source.
Is Feng-GUI still worth using in 2026?
For some workflows, honestly, yes. Its simplicity is a feature, and for quick checks on print, billboards or packaging, a classic saliency pass can be enough. For web pages — where copy, hierarchy and CTAs decide conversions — a model trained on real gaze data plus an actionable audit will take you further.
What does Heatpoints add beyond the heatmap?
The workflow around it: an AI-written audit, Words Map (word-level attention on your copy — nobody else does this), one-click Fix with re-score, weekly monitoring of live pages, a predictive MCP for AI agents, and white-label PDF reports. And our N=93 eye-tracking study found 81% of attention lands outside the hero — the heatmap is where analysis starts, not where it ends.
2007 asked the right question. 2026 answers it better.
Scan any page free — 30 seconds, no account, no watermark.