https://github.com/daniel3303/storyreasoning
공식 코드StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
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/danjun6737/transface
공식 코드TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective
https://github.com/danmohaha/celeb-deepfakeforensics
공식 코드Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
https://github.com/daooshee/HLA-Face-Code
공식 코드HLA-Face: Joint High-Low Adaptation for Low Light Face Detection
https://github.com/dariant/id-booth
공식 코드ID-Booth: Identity-consistent Face Generation with Diffusion Models
https://github.com/dasec/dataset-duplicates
공식 코드Considerations on the Evaluation of Biometric Quality Assessment Algorithms
https://github.com/dasec/hda-synchildfaces-agetransformation
공식 코드Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark
https://github.com/dasec/ladimo
공식 코드LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion
https://github.com/dasec/tetraloss
공식 코드TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks
https://github.com/dash-lab/fakeavceleb
공식 코드FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
https://github.com/datamllab/Mitigating_Gender_Bias_In_Captioning_System
공식 코드https://github.com/datasciencegrimmer/syn-yawpitch
공식 코드https://github.com/david-svitov/margindistillation
공식 코드https://github.com/davidberend/FairnessMatters
공식 코드Fair and accurate age prediction using distribution aware data curation and augmentation
https://github.com/davidcombei/ai4t
공식 코드Unmasking real-world audio deepfakes: A data-centric approach
https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection
공식 코드Combining EfficientNet and Vision Transformers for Video Deepfake Detection
https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformersfor-Video-Deepfake-Detection
공식 코드Combining EfficientNet and Vision Transformers for Video Deepfake Detection
https://github.com/davide-coccomini/Cross-Forgery-Analysis-of-Vision-Transformers-and-CNNs-for-Deepfake-Image-Detection
공식 코드Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
https://github.com/davide-coccomini/adversarial-magnification-to-deceive-deepfake-detection-through-super-resolution
공식 코드Adversarial Magnification to Deceive Deepfake Detection through Super Resolution
https://github.com/davide-coccomini/deepfake-detection-without-deepfakes-generalization-via-synthetic-frequency-patterns-injection
공식 코드Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
https://github.com/davide-coccomini/mintime-multi-identity-size-invariant-timesformer-for-video-deepfake-detection
공식 코드MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection
https://github.com/davidsandberg/facenet
공식 코드https://github.com/ddw2aigroup2cqupt/mgrl
공식 코드Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
https://github.com/ddw2aigroup2cqupt/slfir
공식 코드https://github.com/deepalianeja/CharacterLipSync2D
공식 코드https://github.com/deepbrainai-research/koeba
공식 코드https://github.com/deepcam-cn/facequality
공식 코드EQFace: A Simple Explicit Quality Network for Face Recognition
https://github.com/deepcam-cn/yolov5-face
공식 코드https://github.com/deepfakeil/dfil
공식 코드DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
https://github.com/deepinsight/insightface
공식 코드ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild
공식 코드Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
공식 코드Partial FC: Training 10 Million Identities on a Single Machine
https://github.com/deeplsd/merkel-podcast-corpus
공식 코드https://github.com/deezer/deepfake-detector
공식 코드https://github.com/deezer/sigir2019-2stagesampling
공식 코드Improving Collaborative Metric Learning with Efficient Negative Sampling
https://github.com/delaprada/handsynthesis
공식 코드Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.