https://github.com/HCIILAB/SCUT-FBP5500-Database-Release
공식 코드SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Papers with Code 아카이브
Facial Beauty Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 3
- 보존된 평가 기록
- 5
Facial beauty prediction is the task of predicting the attractiveness of a face.
<span style="color:grey; opacity: 0.6">( Image credit: SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lucasxlu/TransFBP
공식 코드Transferring Rich Deep Features for Facial Beauty Prediction
https://github.com/metavisionlab/metafbp
공식 코드MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
https://github.com/BFD91/facial-beauty-rating-tinder-bot
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/fbplab/MEBeauty-database
https://github.com/lucasxlu/ComboLoss
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/politecat314/MEBeauty_CNN
https://github.com/ptran1203/facial_beauty
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/ugail/FaciaIBeautyIEEESKIMA2023
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.