Natural scene statistics and estimation of shape and reflectance.
Bibliographic record
Abstract
A major function of the visual system is to estimate the shape and reflectance of objects and surfaces from the image. Evidence from both human and computer vision suggests that solutions to this problem involve exploiting prior probability distributions over shape, reflectance and illumination. In an optimal system, these priors would reflect the statistics of our world. To allow a better understanding of the statistics of our environment, and how these statistics shape human perception, we have developed the Southampton-York Natural Scenes (SYNS) public dataset. The dataset includes scene samples from a wide variety of indoor and outdoor scene categories. Each scene sample consists of (i) 3D laser range (LiDAR) data over a nearly spherical field of view, co-registered with (ii) spherical high dynamic range imagery, and (iii) a panorama of stereo image pairs. These data are publicly available at https://syns.soton.ac.uk/. I will discuss a number of challenges that we have addressed in the course of this project, including: 1) geographic sampling strategy, 2) scale selection for surface analysis, 3) relating scene measurements to human perception. I will also discuss future work and potential applications. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".