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/tomlimi/MT-Tokenizer-Bias
공식 코드https://github.com/tongzhou0101/bileve-official
공식 코드Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature
https://github.com/tpulkit/txt2vid
공식 코드Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
https://github.com/tranluan/Nonlinear_Face_3DMM
공식 코드https://github.com/truongvu2000nd/afs
공식 코드https://github.com/tvaranka/fineface
공식 코드Towards Localized Fine-Grained Control for Facial Expression Generation
https://github.com/tvaranka/meb
공식 코드Data Leakage and Evaluation Issues in Micro-Expression Analysis
https://github.com/tyshiwo/FHR_alignment
공식 코드Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
https://github.com/tyshiwo/FSRNet
공식 코드FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/uam-biometrics/FaceQnet
공식 코드FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/uam-biometrics/faceqgen
공식 코드FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment
https://github.com/uark-cviu/micron-bert
공식 코드https://github.com/uds-lsv/author-profiling-prevention-bt
공식 코드Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
https://github.com/umgupta/2d-slice-set-networks-for-brain-age
공식 코드https://github.com/unibas-gravis/Occlusion-Robust-MoFA
공식 코드Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation
https://github.com/unibas-gravis/parametric-face-image-generator
공식 코드Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
https://github.com/usef-kh/fer
공식 코드Facial Emotion Recognition: State of the Art Performance on FER2013
https://github.com/usingcolor/latentswap
공식 코드LatentSwap: An Efficient Latent Code Mapping Framework for Face Swapping
https://github.com/usmancheema89/SejongFaceDatabase
공식 코드https://github.com/uynaes/rankingawareclip
공식 코드Ranking-aware adapter for text-driven image ordering with CLIP
https://github.com/v0lta/pytorch-wavelet-toolbox
공식 코드https://github.com/vcbsl-fairness/fineface
공식 코드FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features
https://github.com/vcbsl/audio-visual-deepfake
공식 코드Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization
https://github.com/vchoutas/expose
공식 코드Monocular Expressive Body Regression through Body-Driven Attention
https://github.com/vcllab/ifqa
공식 코드https://github.com/vdigpku/cmua-watermark
공식 코드CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
https://github.com/verlab/empowering-sign-language
공식 코드https://github.com/viebboy/MultilinearCompressiveLearningFramework
공식 코드https://github.com/viebboy/SIPL
공식 코드https://github.com/vigor-jzhang/dysphonic-emb
공식 코드Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
https://github.com/vineetkumarrakesh/thg
공식 코드https://github.com/vis-face/cdftn
공식 코드Cyclically Disentangled Feature Translation for Face Anti-spoofing
https://github.com/vis-opt-group/sci
공식 코드Toward Fast, Flexible, and Robust Low-Light Image Enhancement
https://github.com/vis-var/lgsc-for-fas
공식 코드https://github.com/vishal3477/Reverse_Engineering_GMs
공식 코드Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.