Evolving sensor environments with visual attention: An experimental exploration
Bibliographic record
Abstract
The research community is experiencing nowadays a significant growth in the amount of sensor data made available to several practical applications, particularly those dealing with visual information. The availability of large datasets poses critical challenges for the selection of only relevant features to allow their timely use and interpretation. The recent years marked an increasing interest in algorithms inspired from biological human vision as an alternative source of ideas for the development of computational resources to deal with large datasets. In particular, computational models of visual attention have been shown to significantly improve the speed of scene understanding and object recognition by attending only the regions of interest and distributing the resources where they are required. This paper explores the use and gauges the performance of visual attention mechanisms for identifying an optimal feature set that ensures the identification and classification of objects in images, in two different scenarios. The first scenario addresses the issue of the identification of different categories of vehicles from multiple viewpoints in a controlled environment, with a relatively known background. The other scenario explores the capabilities of an improved visual attention model for the identification of buildings in satellite imaging, characterized by large variations in content and characteristics and by a cluttered background.
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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 | 0.005 |
| 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".