https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE%2B%2B
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pytorch/vision
https://github.com/qbxlvnf11/face-detection-MTCNN
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/qianjinhao/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/qiaoxiu/facenetRetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/qidiso/mobilefacenet-V2
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/qingyujean/Classification-on-imbalanced-data
https://github.com/qiuyu96/carver
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
https://github.com/quangson05dt1/keras_facenet_ver1
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/qvdang-dev/Facial-Recognition
https://github.com/qvdang-dev/Facial-Recognizance
https://github.com/rahowa/catboost_focal_loss
https://github.com/rahuls02/Image-Noise-Reduction
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/rainofmine/Face_Attention_Network
https://github.com/rajatsahay/Pose2Pose
https://github.com/ram-ch/RealTimeFaceRecognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/random-quark/progressive-gan-completion
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/rap-music/-
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/raspstephan/sugar-flower-fish-or-gravel
https://github.com/raulsteleac/Speech_Emotion_Recognition
Transfer Learning for Improving Speech Emotion Classification Accuracy
https://github.com/raunak222/Facial-Keypint-Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/raunak222/Facial-Keypoint-Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/rdgozum/dexpression-pytorch
DeXpression: Deep Convolutional Neural Network for Expression Recognition
https://github.com/rebeccagio/object_detection
https://github.com/redcaps-dataset/pytorch-retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/reneang17/authorencoder
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/respectableupstandinggentleman/test
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/richaagrawa/Facial-Recognition-System
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/richmondu/libfaceid
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/rikichou/face_recognition_insight_face
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/rmwkwok/product_visual_search
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/rnirdhar/yoloMultiClass
https://github.com/rnirdhar/yoloTestOneClass
https://github.com/rnradon/gender_emotion_classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/rockstar-0000/lip_sync_test
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/rohanbhutani1993/GeneratingDeepfakes
FaceNet: A Unified Embedding for Face Recognition and Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.