https://github.com/machanic/AU_R-CNN
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
FaceNet: A Unified Embedding for Face Recognition and Clustering
RetinaFace: Single-stage Dense Face Localisation in the Wild
RetinaFace: Single-stage Dense Face Localisation in the Wild
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
Very Deep Convolutional Networks for Large-Scale Image Recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
High-Resolution Representations for Labeling Pixels and Regions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.