Novel Segmentation Metrics for Use in Augmented Reality Advertisement Integration
Bibliographic record
Abstract
A major application area for Augmented Reality (AR) is advertisement. To achieve easy advertisement integration and management for stakeholders, a quantitative semantic understanding of the world, that adheres to human perception is necessary. Current deep learning-based segmentation algorithms such as Mask R-CNN provide a mask of the real world (e.g. building facades) in a greedy manner, which, although quantitatively accurate, does not adhere to the needs of the AR advertisers for appropriate ad placement, such as ad size, mask continuity, etc. In this paper, we propose three intuitive metrics for evaluating building facade segmentation specifically for advertisement integration, namely, average discontinuity, normalized continuous area, and resolution ratio. Each of these metrics is inspired from the way advertisers may want to view segmentation results within an AR world editor, or the way users may want to experience AR advertisements. Experimental results on a test segmentation shows the importance of such intuitive metrics, where we show how an accurate placement area for a sample 2D advertisement can be easily isolated from a segmented facade using our metrics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".