Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 26
- 평가표
- 0
- 보존된 평가 기록
- 0
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/microsoft/digiface1m
공식 코드DigiFace-1M: 1 Million Digital Face Images for Face Recognition
https://github.com/miliadis/FIRC
공식 코드Robust and Low-Rank Representation for Fast Face Identification with Occlusions
https://github.com/milliema/invreg
공식 코드https://github.com/ming053l/grace
공식 코드https://github.com/mingwuzheng/imface
공식 코드ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
https://github.com/minhnhatvt/label-distribution-learning-fer-tf
공식 코드Uncertainty-aware Label Distribution Learning for Facial Expression Recognition
https://github.com/minivision-ai/photo2cartoon
공식 코드Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/mintelligence-group/visiophysioenet
공식 코드VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals
https://github.com/miraflow/DistilDIRE
공식 코드DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
https://github.com/mireillefares/vaanimation
공식 코드Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation
https://github.com/mit-ll/lorax_cil
공식 코드LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
https://github.com/mittalgovind/GOTCHA-Deepfakes
공식 코드GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
https://github.com/miu200521358/NeuralVoicePuppetryMMD
공식 코드https://github.com/mk-minchul/adaface
공식 코드https://github.com/mk-minchul/caface
공식 코드Cluster and Aggregate: Face Recognition with Large Probe Set
https://github.com/mk-minchul/dcface
공식 코드DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.