https://github.com/soheilb/multimodal_dictionary_learning
공식 코드Multimodal Task-Driven Dictionary Learning for Image Classification
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.
Multimodal Task-Driven Dictionary Learning for Image Classification
Adaptive Super Resolution For One-Shot Talking-Head Generation
KAN-Based Fusion of Dual-Domain for Audio-Driven Facial Landmarks Generation
A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
ExpLLM: Towards Chain of Thought for Facial Expression Recognition
HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap
Learning Unified Representations for Multi-Resolution Face Recognition
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
Identity-Preserving Aging of Face Images via Latent Diffusion Models
Fair Contrastive Learning for Facial Attribute Classification
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning
Talking Face Generation by Conditional Recurrent Adversarial Network
Re-thinking Model Inversion Attacks Against Deep Neural Networks
Hiding Visual Information via Obfuscating Adversarial Perturbations
SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
BoundaryFace: A mining framework with noise label self-correction for Face Recognition
FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
Controllable 3D Face Generation with Conditional Style Code Diffusion
CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.