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/sciencefans/RSA-for-object-detection
공식 코드https://github.com/sclbd/deepfakebench
공식 코드DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
https://github.com/scu-zjz/forensichub
공식 코드ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
https://github.com/searecluse/cvprw2024
공식 코드https://github.com/seasonSH/LiftedGAN
공식 코드https://github.com/seasonSH/Probabilistic-Face-Embeddings
공식 코드https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect
공식 코드An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
https://github.com/securifai/masked_faces
공식 코드A realistic approach to generate masked faces applied on two novel masked face recognition data sets
https://github.com/sefaburakokcu/finn-quantized-yolo
공식 코드https://github.com/semchan/HyperLips
공식 코드HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation
https://github.com/seva100/stylegan-for-facerec
공식 코드https://github.com/seymayucer/facialphenotypes
공식 코드Measuring Hidden Bias within Face Recognition via Racial Phenotypes
https://github.com/sfzhang15/FaceBoxes
공식 코드https://github.com/sfzhang15/SFD
공식 코드https://github.com/sh1newu/advmark
공식 코드Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics
https://github.com/sh1newu/sepmark
공식 코드SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection
https://github.com/shahroztariq/CLRNet
공식 코드A Convolutional LSTM based Residual Network for Deepfake Video Detection
https://github.com/shaibagon/large_scale_cc
공식 코드https://github.com/shanface33/deepfake_model_attribution
공식 코드https://github.com/shanface33/gpt4mf_ub
공식 코드Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
https://github.com/shangeth/Speaker-Representation-for-Speaker-Profiling
공식 코드Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling
https://github.com/sharpstill/AU_R-CNN
공식 코드AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/shawnxyang/tip-im
공식 코드Towards Face Encryption by Generating Adversarial Identity Masks
https://github.com/shengfly/global-local-transformer
공식 코드https://github.com/sherrycattt/REGDet
공식 코드https://github.com/shighghyujie/newpatch-rl
공식 코드Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
https://github.com/shiluo1990/SFA
공식 코드https://github.com/shiluo1990/bdc
공식 코드Bounding-box deep calibration for high performance face detection
https://github.com/shimmer-ghq/deepfidelity
공식 코드DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
https://github.com/shrezaei/Target-Agnostic-Attack
공식 코드A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
https://github.com/shuchaoduan/qafe-net
공식 코드QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps
https://github.com/shuvenduroy/activefer
공식 코드Active Learning with Contrastive Pre-training for Facial Expression Recognition
https://github.com/shuvenduroy/ssl_fer
공식 코드Analysis of Semi-Supervised Methods for Facial Expression Recognition
https://github.com/shuvenduroy/ssl_fer_ood
공식 코드https://github.com/siddiquelatif/URDU-Dataset
공식 코드Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.