Construction Image Synthetization to Overcome a Small, Biased Real Training Dataset for DNN-Powered Visual Scene Understanding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep neural networks (DNNs) have become a driving factor of visual scene understanding. However, the shortage of construction training images has been a major barrier to fully leverage its maximum performance potential. To address this issue, we investigate the effectiveness of synthetic images on DNN training in a common real-world scenario where only a small, biased real training image dataset is available. To this end, we synthetize numerous construction training images and conduct a DNN training experiment in real construction settings. Results show that the combined dataset-trained model always outperforms the one trained with only a small, biased real dataset. This finding indicates that an image synthetization approach has promising potential to enhance a given real training dataset in terms of data quantity and diversity. Image synthetization with automated labeling will mitigate the training image shortage, contributing to the development of more accurate and scalable DNNs for construction scene understanding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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) | 0.006 | 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