The role of category-specific global orientation statistics for scene categorization
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Bibliographic record
Abstract
Real-world scenes contain category-specific regularities in global orientations that correlate well with broad descriptions of scene content, such as naturalness and openness. Here we test the role of global orientation statistics for scene categorization behavior, using line drawings and photographs of real-world scenes. To this end, we selectively disrupted global orientation distributions or local edge coherence and briefly presented the modified scenes to observers, who were asked to categorize each image as beach, city street, forest, highway, mountain, or office. In Experiment 1, we disrupted global orientations of line drawings by random image rotation, local edge coherence by random contour-shifting, or both. We found that contour-shifting impaired categorization accuracy significantly more than image rotation. When line drawings were under both manipulations, scene categorization was the least accurate. These findings suggest that contour orientation contributes to accurate scene categorization, although less so than local edge coherence. In Experiment 2, we normalized the spectral amplitude of grayscale photographs either within a category to preserve category-specific global orientation distributions or across all six scene categories to remove them. We found that category-specific mean amplitude significantly improved participants' categorization accuracy. How does the distribution of category-specific global orientation affect representational structure of scene categories? Across the two experiments, we compared error patterns for manipulated images with those for the intact conditions. The results showed that the error patterns for the images with global orientations disrupted (image rotation, amplitude normalization within or across categories) showed significant correlation with error patterns for intact images. On the other hand, when localized edge coherence was disrupted (contour shifting with or without image rotation), error patterns did not match those for intact images. We conclude that category-specific global orientation distribution aids in accurate scene categorization, but that it has no impact on the categorical representations underlying human scene perception. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| 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 it