Evaluation of several visual saliency models in terms of gaze prediction accuracy on video
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
A number of methods have been recently proposed to highlight salient regions in images and videos. Considering the importance of attention in video quality evaluation, it would be useful to know how accurate these methods are in terms of predicting viewers' gaze locations in video. However, independent quantitative evaluations of saliency methods are lacking in the current literature. In this paper, we test nine different bottom-up saliency detection models on a set of standard video sequences. The eye-tracking data from 15 viewers for the first and second viewings of a sequence is evaluated against the normalized saliency maps obtained for each frame of the sequence. An accuracy score is determined for each frame and averaged across all frames to provide a measure of performance. For each sequence, the scores of all methods are compared and analyzed statistically to determine if there is a clear winner for that sequence. Further analysis and discussion of the performance of various methods is provided in an attempt to discover which aspects of the saliency models lead to high gaze prediction accuracy.
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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 | 0.002 | 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 | 0.002 |
| 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