Relatable Clothing: Detecting Visual Relationships between People and\n Clothing
Bibliographic record
Abstract
Detecting visual relationships between people and clothing in an image has\nbeen a relatively unexplored problem in the field of computer vision and\nbiometrics. The lack readily available public dataset for ``worn'' and\n``unworn'' classification has slowed the development of solutions for this\nproblem. We present the release of the Relatable Clothing Dataset which\ncontains 35287 person-clothing pairs and segmentation masks for the development\nof ``worn'' and ``unworn'' classification models. Additionally, we propose a\nnovel soft attention unit for performing ``worn'' and ``unworn'' classification\nusing deep neural networks. The proposed soft attention models have an accuracy\nof upward $98.55\\% \\pm 0.35\\%$ on the Relatable Clothing Dataset and\ndemonstrate high generalizable, allowing us to classify unseen articles of\nclothing such as high visibility vests as ``worn'' or ``unworn''.\n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".