Papers with Code 아카이브
Face Detection
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Face Detection is a computer vision task that involves automatically identifying and locating human faces within digital images or videos. It is a fundamental technology that underpins many applications such as face recognition, face tracking, and facial analysis.
<span style="color:grey; opacity: 0.6">( Image credit: insightface )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/daooshee/HLA-Face-Code
공식 코드HLA-Face: Joint High-Low Adaptation for Low Light Face Detection
https://github.com/deepcam-cn/yolov5-face
공식 코드https://github.com/deepinsight/insightface
공식 코드RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/deeplsd/merkel-podcast-corpus
공식 코드https://github.com/dooleys/robustness
공식 코드https://github.com/dooleys/robustness-disparities-in-commercial-face-detection
공식 코드https://github.com/edosedgar/mtcnnattack
공식 코드Real-world adversarial attack on MTCNN face detection system
https://github.com/enkiwang/Imperceptible-fake-face-antiforensic
공식 코드https://github.com/fairdeface/FairDeface
공식 코드FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
https://github.com/fengyuentau/pytorch-flops
공식 코드https://github.com/flakeua/bachelorsthesis
공식 코드Appearance-based gaze estimation enhanced with synthetic images using deep neural networks
https://github.com/fodark/anonygan
공식 코드Graph-based Generative Face Anonymisation with Pose Preservation
https://github.com/golnazghiasi/cofw68-benchmark
공식 코드Occlusion Coherence: Detecting and Localizing Occluded Faces
https://github.com/golnazghiasi/hpm-detection-code
공식 코드Occlusion Coherence: Detecting and Localizing Occluded Faces
https://github.com/grigorisg9gr/deformable_tracking_review_ijcv2016
공식 코드A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
https://github.com/halmstaduniversitybiometrics/lfw-beautified
공식 코드LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
https://github.com/hasanirtiza/Pedestron
공식 코드Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
https://github.com/hertasecurity/LTFT
공식 코드Long-Term Face Tracking for Crowded Video-Surveillance Scenarios
https://github.com/idstcv/mogface
공식 코드https://github.com/idt-iti/lightweight-face-detector-pruning
공식 코드Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
https://github.com/igor-morawski/tmm-sem
공식 코드Leveraging Content and Context Cues for Low-Light Image Enhancement
https://github.com/khurramHashmi/FeatEnHancer
공식 코드FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision
https://github.com/krasjet-yu/yolo-facev2
공식 코드https://github.com/leo7r/swiftface
공식 코드https://github.com/liuwei16/CSP
공식 코드Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
https://github.com/liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild
공식 코드Global Texture Enhancement for Fake Face Detection in the Wild
https://github.com/liwenchao0615/zerrinnet
공식 코드Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition
https://github.com/luxiangju-PersonAI/iCartoonFace
공식 코드https://github.com/lyp-deeplearning/mos-multi-task-face-detect
공식 코드https://github.com/mahehu/TUT-live-age-estimator
공식 코드https://github.com/mdswyz/SISN-Face-Hallucination
공식 코드Face Hallucination via Split-Attention in Split-Attention Network
https://github.com/mgb45/mkfbodytracker_pdaf
공식 코드Single camera pose estimation using Bayesian filtering and Kinect motion priors
https://github.com/mobiface/mobiface.github.io
공식 코드MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
https://github.com/modar7/the_fairness_stitch
공식 코드The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
https://github.com/natanielruiz/dockerface
공식 코드Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.