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/ditzley/joint-gestures-and-face
공식 코드Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters
https://github.com/dkollias/fair-consistent-affect-analysis
공식 코드Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
https://github.com/dnn-security/gan-watermark
공식 코드PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
https://github.com/dominickrei/limited-data-vits
공식 코드Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders
https://github.com/donydchen/FMPN-FER
공식 코드Facial Motion Prior Networks for Facial Expression Recognition
https://github.com/dooleys/fr-nas
공식 코드Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
https://github.com/dooleys/robustness
공식 코드https://github.com/dooleys/robustness-disparities-in-commercial-face-detection
공식 코드https://github.com/dreamflake/odi
공식 코드Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
https://github.com/dreammr/pcl
공식 코드Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
https://github.com/duxiaodan/fast_text2stylegan
공식 코드Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
https://github.com/dxomark-research/piq2023
공식 코드https://github.com/e4s2023/e4s2023
공식 코드E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
https://github.com/e4s2024/e4s2024
공식 코드E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
https://github.com/ebekkers/gsplinets
공식 코드https://github.com/echoanran/on-mitigating-hard-clusters
공식 코드https://github.com/editable-ICLR2020/editable
공식 코드https://github.com/edosedgar/mtcnnattack
공식 코드Real-world adversarial attack on MTCNN face detection system
https://github.com/eduardacaldeira/mst-kd
공식 코드MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
https://github.com/eeskimez/emotalkingface
공식 코드Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
https://github.com/eglxiang/FacialAU
공식 코드Linear Disentangled Representation Learning for Facial Actions
https://github.com/eglxiang/icassp15_emotion
공식 코드Linear Disentangled Representation Learning for Facial Actions
https://github.com/eglxiang/vgg_face
공식 코드https://github.com/eglxiang/ytf
공식 코드https://github.com/ekremerakin/RealWorldOccludedFaces
공식 코드https://github.com/eladrich/pixel2style2pixel
공식 코드Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/eliahuhorwitz/conffusion
공식 코드https://github.com/emalach/UpdateByDisagreement
공식 코드https://github.com/emo-box/emobox
공식 코드EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
https://github.com/engrussman/MAVAE
공식 코드https://github.com/enkiwang/Imperceptible-fake-face-antiforensic
공식 코드https://github.com/ensta-u2is/dido
공식 코드Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
https://github.com/envision-research/gpt4affectivity
공식 코드GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
https://github.com/ergastialex/ldm-diffusion-sem
공식 코드Controllable Face Synthesis with Semantic Latent Diffusion Models
https://github.com/ergastialex/sca-dm
공식 코드Controllable Face Synthesis with Semantic Latent Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.