https://github.com/j-alex-hanson/rethinking-race-face-datasets
공식 코드Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jabumldev/speaker-vgg-cct
공식 코드https://github.com/jackie840129/fedfr
공식 코드FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition
https://github.com/jacobzhaoziyuan/amr-loss
공식 코드https://github.com/jagmohaniiit/3dfacemorph
공식 코드3D Face Morphing Attacks: Generation, Vulnerability and Detection
https://github.com/jagmohaniiit/latentcompositioncode
공식 코드Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
https://github.com/jamespark3922/lsmdc-fillin
공식 코드https://github.com/jamt9000/DVE
공식 코드Unsupervised Learning of Landmarks by Descriptor Vector Exchange
https://github.com/janetlauyeung/crossGENRE4RST
공식 코드Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity
https://github.com/jankolf/grafiqs
공식 코드GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes
https://github.com/jayaneetha/Zeta-Policy
공식 코드A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
https://github.com/jaygshah/Robust-Brain-Age-Prediction
공식 코드Ordinal Classification with Distance Regularization for Robust Brain Age Prediction
https://github.com/jcroh0508/flats
공식 코드Robust Smart Home Face Recognition under Starving Federated Data
https://github.com/jenine-321/genface
공식 코드GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
https://github.com/jenine-321/mfclip
공식 코드MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection
https://github.com/jerryyann/dpi
공식 코드Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolution
https://github.com/jesse1029/Fake-Face-Images-Detection-Tensorflow
공식 코드https://github.com/jessegator/sonar
공식 코드Where are we in audio deepfake detection? A systematic analysis over generative and detection models
https://github.com/jhb86253817/PIPNet
공식 코드Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
https://github.com/jhh37/wearmask3d
공식 코드A 3D model-based approach for fitting masks to faces in the wild
https://github.com/jiahao-uts/slpt-master
공식 코드Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning
https://github.com/jiarenchang/facecycle
공식 코드Learning Facial Representations from the Cycle-consistency of Face
https://github.com/jiawei-ren/BalancedMSE
공식 코드https://github.com/jiaxiangshang/MGCNet
공식 코드Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency
https://github.com/jiaxin-ye/emo-dna
공식 코드Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
https://github.com/jierunchen/tvconv
공식 코드TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing
https://github.com/jimmy-academia/lasgsa
공식 코드https://github.com/jin-s13/COCO-WholeBody
공식 코드https://github.com/jingyang2017/srd_ossl
공식 코드Knowledge Distillation Meets Open-Set Semi-Supervised Learning
https://github.com/jinyugy21/adv-stickers_rhde
공식 코드Adversarial Sticker: A Stealthy Attack Method in the Physical World
https://github.com/jkwang28/honestface
공식 코드HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model
https://github.com/jkwang28/osdface
공식 코드https://github.com/jlee118/nprm
공식 코드https://github.com/jlrex/patt-lite
공식 코드PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
https://github.com/jmpu/deepfaketextdetection
공식 코드https://github.com/jmpu/webconf21-deepfakes-in-the-wild
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.