LookToFocus: Image Focus via Eye Tracking
Bibliographic record
Abstract
We present LookToFocus, a method to perform real-time manual camera focus based on eye tracking. LookToFocus and two alternative methods for manual focus1 photography tasks were compared in a user study. A novel manual focus camera simulation was used to test the methods. The first two methods were LookToFocus and LookToFocusNB (no bounding box). The third method, TapToFocus, used touch for manual focus and image capture, analogous to typical smartphone interaction. LookToFocusNB had the fastest mean capture time at 1429 ms; the mean capture times were 1431 ms for LookToFocusNB and 2416 ms for TapToFocus. When compared to TapToFocus, LookToFocus and LookToFocusNB had faster capture times because these algorithms start converging to the optimal focus as soon as the user spots the target. LookToFocus and LookToFocusNB also had no significant decrease in sharpness error compared to TapToFocus. Users preferred LookToFocus over both LookToFocusNB and TapToFocus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".