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/QingyuZhao/BR-Net
공식 코드Representation Learning with Statistical Independence to Mitigate Bias
https://github.com/RQuispeC/multi-stream-crowd-counting-extended
공식 코드Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
https://github.com/RameshaKaru/devi
공식 코드DEVI: Open-source Human-Robot Interface for Interactive Receptionist Systems
https://github.com/Raschka-research-group/coral-cnn
공식 코드Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/Raul2718/FLUXSynID
공식 코드https://github.com/RenMin1991/SIDE
공식 코드Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
https://github.com/Rock-100/FaceKit
공식 코드Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
https://github.com/Rohith-hacker/realtime-faceid
공식 코드https://github.com/RoyYuchenHe/BlockSparse
공식 코드https://github.com/Rudrabha/LipGAN
공식 코드https://github.com/Rudrabha/Wav2Lip
공식 코드A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/SCL-UT/face-manifold
공식 코드Face Manifold: Manifold Learning for Synthetic Face Generation
https://github.com/SCUT-AILab/DCP
공식 코드Discrimination-aware Network Pruning for Deep Model Compression
https://github.com/SHI-Labs/StyleNAT
공식 코드https://github.com/Seanseattle/MobileFaceSwap
공식 코드MobileFaceSwap: A Lightweight Framework for Video Face Swapping
https://github.com/Shawn-Shan/fawkes
공식 코드Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
https://github.com/Shawn-Shan/trapdoor
공식 코드Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
https://github.com/ShawnXYang/Face-Robustness-Benchmark
공식 코드RobFR: Benchmarking Adversarial Robustness on Face Recognition
https://github.com/SheepBreedingLab-HZAU/LAD-RCNN
공식 코드LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/Star-Clouds/CenterFace
공식 코드CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/Stillrot/RGINP
공식 코드https://github.com/THUIR/ZhihuRec-Dataset
공식 코드A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
https://github.com/TadasBaltrusaitis/OpenFace
공식 코드Convolutional Experts Constrained Local Model for Facial Landmark Detection
https://github.com/TaharChettaoui/FRoundation
공식 코드FRoundation: Are Foundation Models Ready for Face Recognition?
https://github.com/Tencent/TFace
공식 코드SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
https://github.com/Tencent/TFace/tree/Adv-Attack-Defense/security/tasks/Adv-Attack-Defense/Sibling-Attack
공식 코드Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
https://github.com/TencentYoutuResearch/OpenWorld-DeepFakeAttribution
공식 코드Contrastive Pseudo Learning for Open-World DeepFake Attribution
https://github.com/TimoBolkart/GlobalLocalFaceModels
공식 코드Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
https://github.com/TimoBolkart/MultilinearWaveletModel
공식 코드Multilinear Wavelets: A Statistical Shape Space for Human Faces
https://github.com/TimoBolkart/TEMPEH
공식 코드Instant Multi-View Head Capture through Learnable Registration
https://github.com/TimoBolkart/voca
공식 코드https://github.com/ToniCreswell/attribute-cVAEGAN
공식 코드https://github.com/USTC-IMCL/Task-driven-Semantic-Coding-via-RL
공식 코드https://github.com/UTAustin-SwarmLab/SecureSpectra
공식 코드SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures
https://github.com/Usman1021/DLE
공식 코드Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.