https://github.com/bing-li-ai/AniGAN
공식 코드AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
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/bintsi/adaptive-graph-learning
공식 코드Multimodal brain age estimation using interpretable adaptive population-graph learning
https://github.com/bintsi/brain-age-population-graphs
공식 코드https://github.com/biomedia-mbzuai/fetalclip
공식 코드FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
https://github.com/birdwcp/qcs
공식 코드QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
https://github.com/bit-ml/DeCLIP
공식 코드DeCLIP: Decoding CLIP representations for deepfake localization
https://github.com/bit-ml/avh-align
공식 코드Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning
https://github.com/bit-ml/dolos
공식 코드Weakly-supervised deepfake localization in diffusion-generated images
https://github.com/bita-github/MRP-FER
공식 코드https://github.com/bj80heyue/Learning_One_Shot_Face_Reenactment
공식 코드https://github.com/bjzhb666/GS-LoRA
공식 코드https://github.com/bobetocalo/bobetocalo_pami20
공식 코드https://github.com/bodhis4ttva/lhc_net
공식 코드Local Multi-Head Channel Self-Attention for Facial Expression Recognition
https://github.com/bok-bok/MSOC
공식 코드A Multi-Stream Fusion Approach with One-Class Learning for Audio-Visual Deepfake Detection
https://github.com/borrisonxiao/icefall
공식 코드HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
https://github.com/boutiquelee/deepfakeempiricalstudy
공식 코드How Generalizable are Deepfake Image Detectors? An Empirical Study
https://github.com/bovifocr/tcdiff
공식 코드TCDiff: Triple Condition Diffusion Model with 3D Constraints for Stylizing Synthetic Faces
https://github.com/bplank/bleaching-text
공식 코드Bleaching Text: Abstract Features for Cross-lingual Gender Prediction
https://github.com/brain-age/otfpf
공식 코드https://github.com/brkljac/videoface2.0
공식 코드https://github.com/browatbn2/3FabRec
공식 코드https://github.com/bsi-ofiq/ofiq-project
공식 코드Deep Learning-based Compression Detection for explainable Face Image Quality Assessment
https://github.com/bunni90/facepastingattack
공식 코드https://github.com/byangderek/CCF
공식 코드https://github.com/cabani/MaskedFace-Net
공식 코드MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19
https://github.com/caltechvisionlab/frt-rapid-test
공식 코드A Rapid Test for Accuracy and Bias of Face Recognition Technology
https://github.com/canpeng123/fscil_alice
공식 코드Few-Shot Class-Incremental Learning from an Open-Set Perspective
https://github.com/carolineec/EverybodyDanceNow
공식 코드https://github.com/cbsropenproject/6dof_face
공식 코드https://github.com/cc-hpc-itwm/facialgan
공식 코드FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
https://github.com/cc13qq/sapd
공식 코드Detecting Adversarial Faces Using Only Real Face Self-Perturbations
https://github.com/celebv-hq/celebv-hq
공식 코드https://github.com/cgcl-codes/amt-gan
공식 코드Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
https://github.com/chailab-umich/ModelingIndividualEvaluators
공식 코드https://github.com/chandlerbing65nm/FakeImageDetection
공식 코드https://github.com/chaochao-lin/atr-fas
공식 코드Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.