A semi‐supervised approach for building wall layout segmentation based on transformers and limited data
Why this work is in the frame
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Bibliographic record
Abstract
In structural design, accurately extracting information from floor plan drawings of buildings is essential for building 3D models and facilitating design automation. However, deep learning models often face challenges due to their dependence on large labeled datasets, which are labor and time-intensive to generate. And floor plan drawings often present challenges, such as overlapping elements and similar geometric shapes. This study introduces a semi-supervised wall segmentation approach (SWS), specifically designed to perform effectively with limited labeled data. SWS combines a deep semantic feature extraction framework with a hierarchical vision transformer and multi-scale feature aggregation to refine feature maps and maintain the spatial precision necessary for pixel-wise segmentation. SWS incorporates consistency regularization to encourage consistent predictions across weak and strong augmentations of the same image. The proposed method improves an intersection over union by more than 4%.
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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