https://github.com/golnazghiasi/hpm-detection-code
공식 코드Occlusion Coherence: Detecting and Localizing Occluded Faces
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/gongsixue/DebFace
공식 코드Jointly De-biasing Face Recognition and Demographic Attribute Estimation
https://github.com/gonyrosenman/tff
공식 코드https://github.com/google/tf_mesh_renderer
공식 코드https://github.com/grigorisg9gr/deformable_tracking_review_ijcv2016
공식 코드A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
https://github.com/grigorisg9gr/polynomial_nets
공식 코드https://github.com/grip-unina/id-reveal
공식 코드https://github.com/grip-unina/poi-forensics
공식 코드https://github.com/gsethan17/CAPNet_ABAW2021
공식 코드Causal affect prediction model using a facial image sequence
https://github.com/guanghaozhu663/skd-tstsan
공식 코드https://github.com/guanjiyang/sac_jc
공식 코드https://github.com/gufranSabri/FSBI
공식 코드FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images
https://github.com/guillermogotre/cusp
공식 코드https://github.com/guoyang9/class-imbalance-VQA
공식 코드Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
https://github.com/guoyurong0104/crl-code
공식 코드Competing Ratio Loss for Discriminative Multi-class Image Classification
https://github.com/gurayozgur/foundpad
공식 코드FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
https://github.com/gurayozgur/madation
공식 코드MADation: Face Morphing Attack Detection with Foundation Models
https://github.com/gy20073/compact_bilinear_pooling
공식 코드https://github.com/gyh5421/unified_deep_clustering
공식 코드https://github.com/hadushhailu/daal-dml
공식 코드DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning
https://github.com/haibo-qiu/SynFace
공식 코드https://github.com/haibo-qiu/from
공식 코드End2End Occluded Face Recognition by Masking Corrupted Features
https://github.com/haiyuwu/ba-test-dataset
공식 코드What Should Be Balanced in a "Balanced" Face Recognition Dataset?
https://github.com/haiyuwu/celebamso
공식 코드https://github.com/haiyuwu/facebrightness
공식 코드Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
https://github.com/haiyuwu/logicalconsistency
공식 코드Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
https://github.com/haiyuwu/sota-face-recognition-train-and-test
공식 코드https://github.com/halmstaduniversitybiometrics/lfw-beautified
공식 코드LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
https://github.com/hamzah-luqman/mfr
공식 코드Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition
https://github.com/hanbo-cheng/dawn-pytorch
공식 코드https://github.com/hangyu94/ada-cm
공식 코드Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
https://github.com/hanssuny/diffam
공식 코드DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
https://github.com/hanweikung/face_anon_simple
공식 코드https://github.com/hanwu3125/os-sketch
공식 코드One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
https://github.com/haofanwang/accurate-head-pose
공식 코드https://github.com/haoosz/ffr-net
공식 코드A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.