https://github.com/spring-epfl/iccwd
공식 코드A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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
Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
Representation Learning from Limited Educational Data with Crowdsourced Labels
Unsupervised Image Representation Learning with Deep Latent Particles
More comprehensive facial inversion for more effective expression recognition
POSTER++: A simpler and stronger facial expression recognition network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.