Examining visual saliency prediction in naturalistic scenes
Bibliographic record
Abstract
Given the significant number of potential applications, visual saliency has increasingly become an area of interest in image and vision research. Many different strategies for predicting visual saliency have been proposed, that differ in their composition or rationale, and with a significant focus on improving performance across standard benchmarks. Recent benchmarks considering a large number of algorithms have further provided an understanding of the behavior of different algorithms. Performance evaluation has primarily focused on indoor and outdoor images of urban environments, many of which are composed, and contain salient objects. In this work, we test the performance of a number of the better performing algorithms on data derived from naturalistic scenes. In addition, given the strong connection to human vision, we test a putative model for early visual processing in primates tied to spectral energy and normalization. Results demonstrate significant differences between common datasets, and natural images. Performance analysis of the second-order contrast model also provides additional insight concerning the role of spectral energy in determining saliency. Finally we include analysis that demonstrates statistical properties of images that tend to imply common gaze patterns across observers.
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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 | 0.000 | 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 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".