https://github.com/SeongwoongJo/Multimodal-Emotional-Recognition
Frame attention networks for facial expression recognition in videos
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.
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
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
One-shot Face Recognition by Promoting Underrepresented Classes
Progressive Growing of GANs for Improved Quality, Stability, and Variation
MIDV-2019: Challenges of the modern mobile-based document OCR
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.