Detecting Proper Mask Usage with Soft Attention
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
Proper mask usage in public areas has been shown to be critical in the efforts to reduce infection spread in circumstances such as the COVID-19 pandemic. In this paper, we propose mask usage detection approach based on deep learning: a Mask Regional-Convolutional Neural Network (Mask R-CNN) that provides segmentation of faces and masks, and another CNN using a novel Soft Attention unit to detect the correctness of the mask usage. We also provide a small instance segmented subset of the Masked Faces (MAFA) dataset for instance segmentation problems. We use the Mask R-CNN to provide instance segmentations of faces and face masks to the visual relationship detection CNN and predict improperly and properly worn face masks. Various CNN architectures such as ResNet50 were tested and compared to determine its effectiveness for the above task. We evaluate the CNN architectures on accuracy, precision, recall, and specificity of detecting properly worn masks. The best performing network was determined to be the ResNet50V2 architecture with 76.27% accuracy, 84.76% precision, 74.38% recall, and 79.20% specificity.
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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