Texture Map-Based Weak Supervision Improves Facial Wrinkle Segmentation Performance

This paper introduces a weakly supervised learning approach for facial wrinkle segmentation that uses texture map-based pretraining followed by multi-annotator fine-tuning. Rather than requiring extensive pixel-level wrinkle annotations, the model first learns from facial texture maps before being refined on a smaller set of expert-annotated images.

Key technical points: – Two-stage training pipeline: Texture map pretraining followed by multi-annotator supervised fine-tuning – Weak supervision through texture maps allows learning relevant visual features without explicit wrinkle labels – Multi-annotator consensus used during fine-tuning to capture subjective variations in wrinkle perception – Performance improvements over fully supervised baseline models with less labeled training data – Architecture based on U-Net with additional skip connections and attention modules

Results: – Achieved 84.2% Dice score on public wrinkle segmentation dataset – 15% improvement over baseline models trained only on manual annotations – Reduced annotation requirements by ~60% compared to fully supervised approaches – Better generalization to different skin types and lighting conditions

I think this approach could make wrinkle analysis more practical for real-world cosmetic applications by reducing the need for extensive manual annotation. The multi-annotator component is particularly interesting as it acknowledges the inherent subjectivity in wrinkle perception. However, the evaluation on a single dataset leaves questions about generalization across more diverse populations.

I think the texture map pretraining strategy could be valuable beyond just wrinkle segmentation – similar approaches might work well for other medical imaging tasks where detailed annotations are expensive to obtain but related visual features can be learned from more readily available data.

TLDR: Novel weakly supervised approach for facial wrinkle segmentation using texture map pretraining and multi-annotator fine-tuning, achieving strong performance with significantly less labeled data.

Full summary is here. Paper here.