https://github.com/nickyfot/emoclip
공식 코드EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
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.
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
AfriHuBERT: A self-supervised speech representation model for African languages
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
A survey and classification of face alignment methods based on face models
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
xCos: An Explainable Cosine Metric for Face Verification Task
Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
Circumventing Concept Erasure Methods For Text-to-Image Generative Models
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
Real-time Convolutional Neural Networks for Emotion and Gender Classification
Deepfake Detection using Spatiotemporal Convolutional Networks
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
FaceForensics++: Learning to Detect Manipulated Facial Images
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance
On the Exploitation of DCT-Traces in the Generative-AI Domain
FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
Subject-Based Domain Adaptation for Facial Expression Recognition
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.