Revisiting Salient Object Detection: Simultaneous Detection, Ranking,\n and Subitizing of Multiple Salient Objects
Bibliographic record
Abstract
Salient object detection is a problem that has been considered in detail and\nmany solutions proposed. In this paper, we argue that work to date has\naddressed a problem that is relatively ill-posed. Specifically, there is not\nuniversal agreement about what constitutes a salient object when multiple\nobservers are queried. This implies that some objects are more likely to be\njudged salient than others, and implies a relative rank exists on salient\nobjects. The solution presented in this paper solves this more general problem\nthat considers relative rank, and we propose data and metrics suitable to\nmeasuring success in a relative object saliency landscape. A novel deep\nlearning solution is proposed based on a hierarchical representation of\nrelative saliency and stage-wise refinement. We also show that the problem of\nsalient object subitizing can be addressed with the same network, and our\napproach exceeds performance of any prior work across all metrics considered\n(both traditional and newly proposed).\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".