Supporting fashion analysis with AI
Holistic Fashion Classification with Structured LLM Semantics and a Two-Dimensional YIN/YANG Theory
This study uses LLMs to structure a person's facial features, body frame, and the impression of lines, making fashion classification based on YIN/YANG theory easier to reproduce.
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Problem
Fashion analysis depends heavily on expert experience, and different analysts may reach different results for the same person. The challenge is how to support such subjective judgment.
Approach
We reorganized the conventional one-dimensional YIN/YANG classification into two axes: quality of line and maturity. Instead of asking the LLM to classify appearance directly, we have it produce structured descriptions of face shape, body frame, curvature, impression of maturity, and so on.
Key results
Evaluated on data for 100 celebrities, a configuration using GPT-5-mini and kNN achieved 55.00% Top-1 accuracy and a weighted F1 of 56.44. The results point toward using the system as a second opinion rather than a replacement for human judgment.