Large Multimodal Model is a Better Comparator on Facial Beauty Prediction

Zhenyou Liu; Xuefeng Liang; Jian Lin

ICASSP 2025accepted0 citations

Large Multimodal Model is a Better Comparator on Facial Beauty Prediction

Abstract

Order learning has been proven to improve the generalization of facial beauty prediction (FBP) models. However, the scope for advancement remains due to the constraints of current FBP datasets and model scales. In this study, we propose a Large Multimodal Model based Order Learning (LMOL), pioneering the use of a Large Multimodal Model (LMM) as a comparator in order learning. Meanwhile, we develop an FB instruction dataset to fine-tune the LMM, thereby enabling LMOL to discern the FB order. Experiments on three datasets showcase substantial performance improvements in FBP tasks, especially with a notable 18% increase in the Pearson Correlation Coefficient on zero-shot tests (two unseen datasets). This study demonstrate that LMM is a better comparator for FBP tasks.

BibTeX

@inproceedings{icassp2025_largemultimodalm,
  title = {Large Multimodal Model is a Better Comparator on Facial Beauty Prediction},
  author = {Zhenyou Liu and Xuefeng Liang and Jian Lin},
  booktitle = {ICASSP 2025},
  year = {2025}
}