Research on the Design Element Weights of Women's Shirts Based on Visual Perception
Bibliographic record
Abstract
With the continuous change of the consumer market, clothing companies are paying more and more attention to users' experience. Quantifying the users' visual perceptions can help fashion designers to design clothing more scientifically. For the design element weights of women's shirts, this paper takes women aged 18-28 as subjects, and quantifies the subjects' visual perception through eye-tracking experiments. By analyzing the subjects' visual hotspots and key operational index data of ladies' shirts, the weights of each design element are derived. Then the experimental results are compared with the results of the expert hierarchical analysis method, and it is found that they are basically consistent. It shows that by quantifying users' visual perception, it can help fashion designers determine the weights of design elements more conveniently and effectively. Thus, it helps service designers to carry out personalized clothing design, and has important research significance for improving user satisfaction and design hit rate.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; both teacher heads agree on what is shown here.
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".