https://github.com/shrezaei/Target-Agnostic-Attack
공식 코드A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
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/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
https://github.com/silence-tang/artnerf
공식 코드ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
https://github.com/silvialaurapintea/reg-cls
공식 코드A step towards understanding why classification helps regression
https://github.com/simogroup/3dfair
공식 코드3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes
https://github.com/skjack/diffusionfake
공식 코드DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion
https://github.com/skrantidatta/lipinc
공식 코드https://github.com/skrantidatta/lipinc-v2
공식 코드Detecting Lip-Syncing Deepfakes: Vision Temporal Transformer for Analyzing Mouth Inconsistencies
https://github.com/skylar-sutherland/single-scan-3dmms
공식 코드https://github.com/slundberg/shap
공식 코드https://github.com/smile-ffg/he-man-concrete
공식 코드HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models
https://github.com/smuelpeng/sgps-noisefreedml
공식 코드https://github.com/socialabubi/iFakeFaceDB
공식 코드GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
https://github.com/sohailahmedkhan/CLIPping-the-Deception
공식 코드CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
https://github.com/soheilb/multimodal_dictionary_learning
공식 코드Multimodal Task-Driven Dictionary Learning for Image Classification
https://github.com/somuchtome/faceswap
공식 코드https://github.com/songluchuan/adasr-talkinghead
공식 코드Adaptive Super Resolution For One-Shot Talking-Head Generation
https://github.com/sowmen/face-cutout
공식 코드https://github.com/sowwnn/KFusion-Dual-Domain-for-Speech-to-Landmarks
공식 코드KAN-Based Fusion of Dual-Domain for Audio-Driven Facial Landmarks Generation
https://github.com/spaghettisystems/emotion_whisper
공식 코드https://github.com/spring-epfl/iccwd
공식 코드A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
https://github.com/sridharsola/msd-e
공식 코드https://github.com/sstzal/DFRF
공식 코드Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
https://github.com/sstzal/DiffTalk
공식 코드DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
https://github.com/starhiking/ExpLLM-TMM
공식 코드ExpLLM: Towards Chain of Thought for Facial Expression Recognition
https://github.com/starhiking/HeatmapInHeatmap
공식 코드HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap
https://github.com/stat-ml/face-evaluation
공식 코드https://github.com/stefhoer/PartialLFW
공식 코드https://github.com/stephenivy07/Landmarkgan
공식 코드https://github.com/stevensmith2000/btnet
공식 코드Learning Unified Representations for Multi-Resolution Face Recognition
https://github.com/stevexwu/face_brightness
공식 코드Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
https://github.com/storymy/take-off-eyeglasses
공식 코드Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.