https://github.com/PJLab-ADG/driveLikeAHuman
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PJunhyuk/exercise-pose-analyzer
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/PJunhyuk/people-counting-pose
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/PKU-YuanGroup/UniWorld-V1
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
https://github.com/Pacific-cyber/KUKA_Catch_Project
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter04
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter05
https://github.com/PaddlePaddle/FleetX
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
https://github.com/PaddlePaddle/PaddleClas
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleMIX
https://github.com/PaddlePaddle/PaddleNLP
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/pipelines/examples/text_to_image
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/PaddlePaddle/PaddleSeg
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/PaddlePaddle/PaddleSpeech
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Pandade1997/tera_asvproof
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
https://github.com/ParadoxicalNerd/Style-Transfer-Implementation
https://github.com/ParitoshParmar/MTL-AQA
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
https://github.com/Pavan-pk/ArtGan_Realism
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Pea-Shooter/ADNet
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/Pengxiao-Wang/Typeface-and-Font-Style-Transfer
https://github.com/PetiteBeast/pytorch_style_transfer
https://github.com/PetruninAlex/Sketch2Real
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Pillercottrer/radcap_project
https://github.com/PingoLH/CenterNet-HarDNet
https://github.com/Poseidon0711/Art-Generation-with-Neural-Style-Transfer
https://github.com/Powercoder64/TAA-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Pranav-Patil-7/Automatic_Image_Captioning_Project
https://github.com/PranavMahajan25/Neural-ODE
https://github.com/PrashanthaTP/wav2mov
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/ProGamerGov/neural-style-pt
https://github.com/ProofByConstruction/texture-networks
https://github.com/PruneTruong/GLU-Net
Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/Pulkitdzrt/ML-Image-Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.