A Deep Neural Network for Image Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Many tasks demand high-quality remote sensing image annotation products that are difficult to achieve through existing automated methods. Obtaining high-quality pixel annotations is time-consuming and laborious. This study proposes architecture with controllable correction ability that can automatically generate image annotations and allow annotators to adaptively correct previous annotations by making simple guidance information after discovering errors. This method can be applied to any convolution-based network. A training method and metric were proposed to measure the efficiency of re-annotation. We conducted experiments on the Vaihingen dataset using different base architectures and backbones. Our study shows that our training method can effectively direct the guidance module to utilize the guidance information and improve the re-annotation efficiency up to 2.53 times. In addition, more advanced architectures may give better results.
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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 | 0.001 | 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 | 0.004 |
| 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