https://github.com/Sakib1263/VGG-1D-2D-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
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
FaceNet: A Unified Embedding for Face Recognition and Clustering
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
Frame attention networks for facial expression recognition in videos
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.