Significance of Bottom-up Attributes in Video Saliency Detection Without Cognitive Bias
Bibliographic record
Abstract
Saliency in an image or video is the region of interest that stands out relative to its neighbors and consequently attracts more human attention. To determine the salient areas within a scene, visual importance and distinctiveness of the regions must be measured. A key factor in designing saliency detection algorithms for videos is to understand how different visual cues affect the human perceptual and visual system. To this end, we investigated the bottom-up features including color, texture, and motion in video sequences for both one-by-one and combined scenarios to provide a ranking system stating the most dominant circumstances for each feature individually and in combination with other features as well. In this work, we only considered the individual features and various visual saliency attributes investigated under conditions in which we had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgements and decision-making actions. Since computers do not typically have this ability, we tried to minimize this bias in the design of our experiment. First, we modelled our test data as 2D images and videos in a virtual environment to avoid any cognitive bias. Then, we performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed ranking system of salient visual attention stimuli was achieved using an eye tracking procedure. This work provides a benchmark to specify the most salient stimulus with comprehensive information.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".