Efficient eye pointing with a fisheye lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper evaluates refinements to existing eye pointing techniques involving a fisheye lens. We use a fisheye lens and a video-based eye tracker to locally magnify the display at the point of the user’s gaze. Our gaze-contingent fisheye facilitates eye pointing and selection of magnified (expanded) targets. Two novel interaction techniques are evaluated for managing the fisheye, both dependent on real-time analysis of the user’s eye movements. Unlike previous attempts at gaze-contingent fisheye control, our key innovation is to hide the fisheye during visual search, and morph the fisheye into view as soon as the user completes a saccadic eye movement and has begun fixating a target. This style of interaction allows the user to maintain an overview of the desktop during search while selectively zooming in on the foveal region of interest during selection. Comparison of these interaction styles with ones where the fisheye is continuously slaved to the user’s gaze (omnipresent) or is not used to affect target expansion (nonexistent) shows performance benefits in terms of speed and accuracy. Key words: Eye Pointing, Fisheyes 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it