https://github.com/mk-minchul/dcface
공식 코드DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Papers with Code 아카이브
Facial Recognition and Modelling
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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/mk-minchul/kprpe
공식 코드https://github.com/mkocaoglu/CausalGAN
공식 코드CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
https://github.com/mobiface/mobiface.github.io
공식 코드MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
https://github.com/modar7/the_fairness_stitch
공식 코드The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
https://github.com/modelscope/facechain
공식 코드TopoFR: A Closer Look at Topology Alignment on Face Recognition
https://github.com/mohammadrezanaderi4/sfi-swin
공식 코드https://github.com/monu-khicher-1/multi-stage-learning
공식 코드https://github.com/mosamdabhi/3dlfm
공식 코드https://github.com/mouse1998mouse/S-xception
공식 코드Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection
https://github.com/mrkoujan/DeepFaceFlow
공식 코드https://github.com/mrkoujan/FER
공식 코드Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
https://github.com/msa-lmc/s2d
공식 코드https://github.com/msed-ebrahimi/aroface
공식 코드ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition
https://github.com/mshaikh2/HDL_Forensics
공식 코드https://github.com/msy1412/abaw4
공식 코드AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.