https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet
https://github.com/2024-MindSpore-1/Code5/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2024-MindSpore-1/Code5/tree/main/AlignedReID
Domain adaptation for person re-identification on new unlabeled data using AlignedReID++
https://github.com/2024-MindSpore-1/Code7/tree/main/cdp
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
https://github.com/2024-MindSpore-1/Code7/tree/main/centerface
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/2275519762/RetinaFace_Pytorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/394781865/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/3P2S/arcface
https://github.com/497626895/darknet
https://github.com/4uiiurz1/pytorch-adacos
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
https://github.com/771979972/Paddle_pSp
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/771979972/paddle-SAM
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
https://github.com/9B8DY6/Landmark-detection-in-TOF-MRA
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
https://github.com/AI-Guru/pointcloud_experiments
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AIOSYM/dl-model-gallery
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AKASH2907/Content-based-Video-Recommendation
https://github.com/AKASH2907/Content-based-Video-Relevance-Prediction
https://github.com/Aal-Izz-Well/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/AbdallahMGomaa/PatchNet
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
https://github.com/Abdelhamid-bouzid/Deep-metric-learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/AbdielNie/GAN_Face-Rotation
https://github.com/AbhinavBaranwal/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/AbhinavJhanwar/GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/AdamHtooLwin/vigilant
https://github.com/Adarsh79/Face-Recognition-with-Facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/AdeelH/pytorch-multi-class-focal-loss
https://github.com/Adityaraj975/DFL
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/Adityaraj975/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/AdivarekarBhumit/ID-Card-Segmentation
https://github.com/AdrianUng/keras-triplet-loss-mnist
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/AdrianVG194/cnn-hyperopt-fer2013
https://github.com/AemikaChow/DATASOURCE
Imbalanced Deep Learning by Minority Class Incremental Rectification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.