Papers with Code 아카이브
Facial Recognition and Modelling
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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/hangyu94/ada-cm
공식 코드Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
https://github.com/hanssuny/diffam
공식 코드DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
https://github.com/hanweikung/face_anon_simple
공식 코드https://github.com/hanwu3125/os-sketch
공식 코드One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
https://github.com/haofanwang/accurate-head-pose
공식 코드https://github.com/haoosz/ffr-net
공식 코드A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
https://github.com/haoxin917/3dface
공식 코드3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
https://github.com/happy-jihye/Cartoon-StyleGan2
공식 코드https://github.com/happynear/AMSoftmax
공식 코드https://github.com/happynear/NormFace
공식 코드https://github.com/happynear/PainRegression
공식 코드Regularizing Face Verification Nets For Pain Intensity Regression
https://github.com/hardik-uppal/teacher-student-gan
공식 코드Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition
https://github.com/harlanhong/actalker
공식 코드https://github.com/harlanhong/cvpr2022-dagan
공식 코드Depth-Aware Generative Adversarial Network for Talking Head Video Generation
https://github.com/harlanhong/iccv2023-mcnet
공식 코드https://github.com/hasanhuz/SpanEmo
공식 코드SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
https://github.com/hasanirtiza/Pedestron
공식 코드Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
https://github.com/hashtag00002/tt-df
공식 코드TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection
https://github.com/heathcliff-saku/dopatch
공식 코드Distributional Modeling for Location-Aware Adversarial Patches
https://github.com/hechmik/voxceleb_enrichment_age_gender
공식 코드https://github.com/helme/ecg_ptbxl_benchmarking
공식 코드Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
https://github.com/hengruilou/deepfacegen
공식 코드A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
https://github.com/hengxyz/Dynamic_multi-task-learning
공식 코드Dynamic Multi-Task Learning for Face Recognition with Facial Expression
https://github.com/hengxyz/cari-visual-recognition-via-multitask-learning
공식 코드Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition
https://github.com/heqianpei/diga
공식 코드Distributionally Generative Augmentation for Fair Facial Attribute Classification
https://github.com/hertasecurity/LTFT
공식 코드Long-Term Face Tracking for Crowded Video-Surveillance Scenarios
https://github.com/highwaywu/st-ddl
공식 코드Exploring Spatial-Temporal Features for Deepfake Detection and Localization
https://github.com/hjy1312/AGPM
공식 코드Attention-guided Progressive Mapping for Profile Face Recognition
https://github.com/hm-choi/blind-match
공식 코드https://github.com/honghanh2104/t2a-think-twice-before-adaptation
공식 코드https://github.com/hongshuochen/DefakeHop
공식 코드DefakeHop: A Light-Weight High-Performance Deepfake Detector
https://github.com/hotfinda/Compensation-sampling
공식 코드Compensation Sampling for Improved Convergence in Diffusion Models
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huangjiadidi/DeepFakeMnist
공식 코드https://github.com/huangyangyu/ADNet
공식 코드ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.