Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/msy1412/abaw4
공식 코드AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition
https://github.com/murinj/big-moe
공식 코드BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
https://github.com/murphytju/difffas
공식 코드https://github.com/muse1998/IAL-for-Facial-Expression-Recognition
공식 코드Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild
https://github.com/muse1998/mmnet
공식 코드MMNet: Muscle motion-guided network for micro-expression recognition
https://github.com/mvillarreal14/confoc
공식 코드ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
https://github.com/mysee1989/GraphJigsaw
공식 코드https://github.com/mzhang367/opqn
공식 코드Orthonormal Product Quantization Network for Scalable Face Image Retrieval
https://github.com/naserdamer/mordiff
공식 코드https://github.com/natanielruiz/deep-head-pose
공식 코드https://github.com/natanielruiz/dockerface
공식 코드Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
https://github.com/nathanlem1/igae-net
공식 코드https://github.com/nchinaev/MobileFace
공식 코드MobileFace: 3D Face Reconstruction with Efficient CNN Regression
https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/ndb796/machineunlearning
공식 코드https://github.com/ndido98/frcsyn
공식 코드FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
https://github.com/ndrwmlnk/ai-blitz-xiii
공식 코드https://github.com/ndrwmlnk/deep-face-generation-and-editing-a-survey
공식 코드https://github.com/negarhdr/diversified-facial-expression-recognition
공식 코드Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
https://github.com/nenadmarkus/pico
공식 코드Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/nerminsamet/HPRNet
공식 코드HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation
https://github.com/netopedro/idistill
공식 코드Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
https://github.com/netopedro/ocfr-2022
공식 코드https://github.com/netopedro/orthomad
공식 코드OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
https://github.com/neuralchen/SimSwap
공식 코드SimSwap: An Efficient Framework For High Fidelity Face Swapping
https://github.com/neuromorphic-paris/frog
공식 코드A Framework for Event-based Computer Vision on a Mobile Device
https://github.com/nhshin-mcl/mwr
공식 코드Moving Window Regression: A Novel Approach to Ordinal Regression
https://github.com/niccobiondi/cores-compatibility
공식 코드https://github.com/nice-x/dfgc-2022
공식 코드https://github.com/nicksunday/deepfake-music-detector
공식 코드https://github.com/nickyfot/emoclip
공식 코드EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
https://github.com/nii-yamagishilab/AfriHuBERT
공식 코드AfriHuBERT: A self-supervised speech representation model for African languages
https://github.com/nii-yamagishilab/antideepfake
공식 코드https://github.com/nii-yamagishilab/ssl_vits_df
공식 코드Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
https://github.com/nilslukas/gan-watermark
공식 코드PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.