https://github.com/sohailahmedkhan/CLIPping-the-Deception
공식 코드CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
Papers with Code 아카이브
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/soheilb/multimodal_dictionary_learning
공식 코드Multimodal Task-Driven Dictionary Learning for Image Classification
https://github.com/somuchtome/faceswap
공식 코드https://github.com/songluchuan/adasr-talkinghead
공식 코드Adaptive Super Resolution For One-Shot Talking-Head Generation
https://github.com/sowmen/face-cutout
공식 코드https://github.com/sowwnn/KFusion-Dual-Domain-for-Speech-to-Landmarks
공식 코드KAN-Based Fusion of Dual-Domain for Audio-Driven Facial Landmarks Generation
https://github.com/spaghettisystems/emotion_whisper
공식 코드https://github.com/spring-epfl/iccwd
공식 코드A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
https://github.com/sridharsola/msd-e
공식 코드https://github.com/sstzal/DFRF
공식 코드Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
https://github.com/sstzal/DiffTalk
공식 코드DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
https://github.com/starhiking/ExpLLM-TMM
공식 코드ExpLLM: Towards Chain of Thought for Facial Expression Recognition
https://github.com/starhiking/HeatmapInHeatmap
공식 코드HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap
https://github.com/stat-ml/face-evaluation
공식 코드https://github.com/stefhoer/PartialLFW
공식 코드https://github.com/stephenivy07/Landmarkgan
공식 코드https://github.com/stevensmith2000/btnet
공식 코드Learning Unified Representations for Multi-Resolution Face Recognition
https://github.com/stevexwu/face_brightness
공식 코드Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
https://github.com/storymy/take-off-eyeglasses
공식 코드Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
https://github.com/sudban3089/ID-Preserving-Facial-Aging
공식 코드Identity-Preserving Aging of Face Images via Latent Diffusion Models
https://github.com/sungho-coolg/fscl
공식 코드Fair Contrastive Learning for Facial Attribute Classification
https://github.com/sunwei925/dn-piqa
공식 코드https://github.com/sunyiyou/safas
공식 코드Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
https://github.com/superhero-7/DreamID
공식 코드DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning
https://github.com/susanqq/Talking_Face_Generation
공식 코드Talking Face Generation by Conditional Recurrent Adversarial Network
https://github.com/sutd-visual-computing-group/Re-thinking_MI
공식 코드Re-thinking Model Inversion Attacks Against Deep Neural Networks
https://github.com/suzhigangssz/avih
공식 코드Hiding Visual Information via Obfuscating Adversarial Perturbations
https://github.com/svddchallenge/ctrsvdd2024_baseline
공식 코드SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
https://github.com/swjtu-3dvision/boundaryface
공식 코드BoundaryFace: A mining framework with noise label self-correction for Face Recognition
https://github.com/swjtuma/fer-yolo-mamba
공식 코드FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
https://github.com/swordcheng/FCSR-GAN
공식 코드FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
https://github.com/sxjdwang/talklip
공식 코드Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
https://github.com/sxl142/tex-face
공식 코드Controllable 3D Face Generation with Conditional Style Code Diffusion
https://github.com/szlbiubiubiu/CemiFace
공식 코드CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
https://github.com/szlbiubiubiu/Part_fViT
공식 코드https://github.com/szlbiubiubiu/lafs_cvpr2024
공식 코드LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.