https://github.com/bghojogh/Face-Gender-Recognition-Fusion
공식 코드A Fusion-based Gender Recognition Method Using Facial Images
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bghojogh/Pixel-Aligned-Face-Recognition
공식 코드Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
https://github.com/bghojogh/Roweis-Discriminant-Analysis
공식 코드Roweis Discriminant Analysis: A Generalized Subspace Learning Method
https://github.com/bghojogh/Roweisposes
공식 코드Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
https://github.com/bghojogh/Weighted-Fisher-Discriminant-Analysis
공식 코드Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
https://github.com/bidalab/chatgpt_facebiometrics
공식 코드https://github.com/bieltura/Automotive_face_detector
공식 코드Low cost enhanced security face recognition with stereo cameras
https://github.com/billyxyb/facednerf
공식 코드FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
https://github.com/billyxyb/fdnerf
공식 코드FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
https://github.com/bilylee/DCQ
공식 코드Dynamic Class Queue for Large Scale Face Recognition In the Wild
https://github.com/bing-li-ai/AniGAN
공식 코드AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.