UNISAL — how our attention model works.

1. What UNISAL is

UNISAL (UNified Image and video SALiency) is a saliency model published by Drost et al. at ECCV 2020 — peer-reviewed, open source, and still one of the strongest architectures for predicting where human eyes land on an image. 'Saliency' is the computer-vision term for visual attention: given pixels, predict the probability that a human fixates each region in the first seconds of viewing.

Its distinctive trait is unified training: one compact network (a MobileNetV2 backbone with domain-adaptive layers) trained jointly on image AND video gaze data. That joint training matters for web pages, which sit between the two worlds: static layout, but scanned by the eye in a sequence.

2. What it learned from

The model never saw your page before. What it carries is the statistics of thousands of real human gazes, recorded in controlled studies and published as public research datasets: SALICON (10,000 images with attention annotations), MIT1003/MIT300 (the canonical eye-tracking benchmark), CAT2000, and video datasets (DHF1K, Hollywood-2, UCF-Sports) recorded with real eye trackers.

From those recordings it learned the regularities of human attention: contrast pulls, faces capture, large text weighs, isolated elements win, centers bias. Your scan applies those regularities to your pixels.

3. What we compute on top

UNISAL outputs a gray probability map. Everything else is our layer, and it's deliberately simple and public: attention intensity (mean saliency — typical pages sit at 5-25%), focus clarity (does attention pool on one point or smear?), visual hierarchy (can the eye rank elements?), coverage (how much surface engages the eye), and the primary gaze zone on a 3×3 grid.

The score you see everywhere is: 0.35 × attention intensity (×4, capped at 100) + 0.35 × hierarchy + 0.30 × focus clarity. No hidden weights, no per-customer tuning. The AI-written parts of the report (audit, copy analysis) interpret these numbers — they never change them.

4. Why determinism matters

The same screenshot always produces the same map and the same score. That sounds obvious; it isn't — LLM-judged 'design scores' drift between runs. Determinism is what makes the score usable as an instrument: fix your page, re-scan, and the delta measures YOUR change. It's also why the score works in agent loops and CI (see our MCP server and API).

5. What it cannot do (honestly)

It predicts the first seconds of attention of a first-time viewer — not minutes of task-driven browsing, not brand familiarity, not your specific audience's habits. It reads pixels: text is shape and contrast to it (that's why our Words Map runs a separate language-level analysis). And a great attention score doesn't guarantee conversion — it guarantees the eye finds your message; the message still has to be good.

We deliberately don't market an accuracy percentage. Saliency benchmarks are public (MIT300 et al.) and UNISAL's numbers are in the paper; on YOUR site, the honest verification is our Pulse module — it measures real visitors cookieless and confronts prediction with reality, page by page.