https://github.com/uynaes/rankingawareclip
공식 코드Ranking-aware adapter for text-driven image ordering with CLIP
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/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-opt-group/sci
공식 코드Toward Fast, Flexible, and Robust Low-Light Image Enhancement
https://github.com/vishal3477/Reverse_Engineering_GMs
공식 코드Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
https://github.com/visionjo/facerec-bias-bfw
공식 코드https://github.com/visteam-isr-uc/ylfw
공식 코드Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition
https://github.com/visym/keynet
공식 코드Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
https://github.com/vitoralbiero/afd_dataset_cleaned
공식 코드https://github.com/vitoralbiero/gender_balance_training_data
공식 코드How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
https://github.com/vitoralbiero/img2pose
공식 코드img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/vitoralbiero/vggface2_test_cleaned
공식 코드https://github.com/vladhondru25/exddv
공식 코드ExDDV: A New Dataset for Explainable Deepfake Detection in Video
https://github.com/vprlab/iexam
공식 코드iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition
https://github.com/vpsg-research/waveguard
공식 코드https://github.com/vrkh1996/IPU-Net
공식 코드https://github.com/vsingh-group/LCODEC-deep-unlearning
공식 코드Deep Unlearning via Randomized Conditionally Independent Hessians
https://github.com/vuvko/fitw2020
공식 코드Achieving Better Kinship Recognition Through Better Baseline
https://github.com/vvictoryuki/freedom
공식 코드FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model
https://github.com/wagner-niklas/cage_expression_inference
공식 코드https://github.com/wang-zidu/3ddfa-v3
공식 코드3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation
https://github.com/wang-zidu/s2td-face
공식 코드S2TD-Face: Reconstruct a Detailed 3D Face with Controllable Texture from a Single Sketch
https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection
공식 코드M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
https://github.com/wangsuzhen/Audio2Head
공식 코드Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion
https://github.com/wangty1/DCPT
공식 코드Deep Convolutional Pooling Transformer for Deepfake Detection
https://github.com/wangty1/LampMark
공식 코드LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks
https://github.com/wangxuan2401/3dfacerecon
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.