Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection
<|reference_start|>Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection: 1. Research question: With the growing interest in skin diseases and skin aesthetics, the ability to predict facial wrinkles is becoming increasingly important. This study aims to evaluate whether a computational model, convolutional neural networks (CNN), can be trained for automated facial wrinkle segmentation. 2. Findings: Our study presents an effective technique for integrating data from multiple annotators and illustrates that transfer learning can enhance performance, resulting in dependable segmentation of facial wrinkles. 3. Meaning: This approach automates intricate and time-consuming tasks of wrinkle analysis with a deep learning framework. It could be used to facilitate skin treatments and diagnostics.<|reference_end|>
@article{moon2024weakly,
    title={Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning
  for Facial Wrinkle Detection},
    author={Ik Jun Moon, Junho Moon, Ikbeom Jang},
    journal={arXiv preprint arXiv:2408.09952},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09952},
    primaryClass={cs.CV cs.AI cs.LG}
}