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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/ZichenMiao/L3Net
공식 코드https://github.com/Zielon/MICA
공식 코드https://github.com/ZiqiaoPeng/SyncTalk
공식 코드SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
https://github.com/ZitongYu/CDCN
공식 코드Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/ZitongYu/DeepFAS
공식 코드https://github.com/aakash4305/gbdf
공식 코드GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
https://github.com/aaroncomo/lipfd
공식 코드https://github.com/abacusai/intraprocessing_debiasing
공식 코드https://github.com/abcdef2000/R2RNet
공식 코드R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
https://github.com/abdallahdib/NextFace
공식 코드Practical Face Reconstruction via Differentiable Ray Tracing
https://github.com/abdelkadermh/isarcasmeval
공식 코드https://github.com/abheesht17/super-pixels
공식 코드Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
https://github.com/abhi1kumar/LUVLi
공식 코드LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
https://github.com/abhinavdhall/deepfake
공식 코드Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
https://github.com/abrarmajeedi/Disguised-Facial-Recognition-DFR
공식 코드A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
https://github.com/adamian98/pulse
공식 코드PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/adauchendu/topformer
공식 코드TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
https://github.com/adipiz99/RawNetLite
공식 코드https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
공식 코드https://github.com/adobe-research/videosham-dataset
공식 코드Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
https://github.com/adrianastan/tada
공식 코드TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
https://github.com/advboxes/AdvBox
공식 코드Advbox: a toolbox to generate adversarial examples that fool neural networks
https://github.com/advcolor123/advcolor
공식 코드Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
https://github.com/adversarial-for-goodness/APF
공식 코드https://github.com/ae-1129/lanmsff
공식 코드https://github.com/afm215/FaVGen
공식 코드Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
https://github.com/ah651/deepfake_adv
공식 코드https://github.com/ahmedelgazzar0/dast-gcn
공식 코드Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
https://github.com/ai-application-and-integration-lab/dgua_fas
공식 코드https://github.com/ai-forever/ghost
공식 코드A new face swap method for image and video domains: a technical report
https://github.com/ai-s2-lab/m2s-add
공식 코드Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
https://github.com/ailon-island/facke
공식 코드https://github.com/aimagelab/COCOFake
공식 코드Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
https://github.com/aimagelab/code
공식 코드Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
https://github.com/aimultimedialab/favci2d-face-verification-with-challenging-imposters-and-diversified-demographics
공식 코드Face Verification with Challenging Imposters and Diversified Demographics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.