Summary of Facial Wrinkle Segmentation For Cosmetic Dermatology: Pretraining with Texture Map-based Weak Supervision, by Junho Moon et al.
Facial Wrinkle Segmentation for Cosmetic Dermatology: Pretraining with Texture Map-Based Weak Supervision
by Junho Moon, Haejun Chung, Ikbeom Jang
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solutions address the challenges of facial wrinkle detection in cosmetic dermatology, including the need for precise and consistent segmentation. A novel public dataset, ‘FFHQ-Wrinkle’, is introduced, comprising 1,000 labeled images and 50,000 weakly labeled images. This dataset serves as a foundation for developing advanced wrinkle detection algorithms. A simple training strategy utilizing texture maps is also proposed, allowing various segmentation models to detect wrinkles across the face. The strategy involves pretraining models on large datasets with weak labels or masked texture maps, followed by finetuning using human-labeled data. This approach demonstrates improved segmentation performance in facial wrinkle segmentation compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Facial wrinkle detection is important in cosmetic dermatology. Currently, manual segmentation of wrinkles is time-consuming and subjective, leading to inconsistent results. To solve this problem, researchers created a new dataset called ‘FFHQ-Wrinkle’ with 1,000 labeled images and 50,000 weakly labeled images. This dataset can be used by other researchers to develop better wrinkle detection algorithms. The team also came up with a simple training strategy that uses texture maps to detect wrinkles across the face. This approach works by first pretraining models on large datasets with weak labels or masked texture maps, and then fine-tuning them using human-labeled data. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining
