Recurrent Refinement for Visual Saliency Estimation in Surveillance Scenarios
Bibliographic record
Abstract
In recent years, many different proposals for visual saliency computation have been put forth, that generally frame the determination of visual saliency as a measure of local feature contrast. There is however, a paucity of approaches that take into account more global holistic elements of the scene. In this paper, we propose a novel mechanism that augments the visual representation used to compute saliency. Inspired by research into biological vision, this strategy is one based on the role of recurrent computation in a visual processing hierarchy. Unlike existing approaches, the proposed model provides a manner of refining local saliency based computation based on the more global composition of a scene that is independent of semantic labeling or viewpoint. The results presented demonstrate that a fast recurrent mechanism significantly augments the determination of salient regions of interest as compared with a purely feed forward visual saliency architecture. This demonstration is applied to the problem of detecting targets of interest in various surveillance scenarios.
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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 | 0.001 | 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 | 0.001 |
| 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".