https://github.com/Mikexu007/PCRP
공식 코드Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MohsenFayyaz89/T3D
공식 코드Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
공식 코드Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
https://github.com/NJUVISION/DHVC
공식 코드High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
공식 코드https://github.com/NUS-HPC-AI-Lab/VideoSys
공식 코드https://github.com/NUST-Machine-Intelligence-Laboratory/HFAN
공식 코드Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
https://github.com/NVIDIA/semantic-segmentation
공식 코드SDCNet: Video Prediction Using Spatially-Displaced Convolution
https://github.com/NVIDIA/vid2vid
공식 코드https://github.com/NVlabs/conv-tt-lstm
공식 코드Convolutional Tensor-Train LSTM for Spatio-temporal Learning
https://github.com/NYCU-MAPL/B-CANF
공식 코드B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
https://github.com/NagabhushanSN95/DeCOMPnet
공식 코드https://github.com/NagabhushanSN95/PVQA
공식 코드https://github.com/Nathan-Li123/SMOTer
공식 코드https://github.com/Necolizer/CHASE
공식 코드CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
https://github.com/Necolizer/ISTA-Net
공식 코드https://github.com/NellyElsayed/rgcLSTM
공식 코드Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
https://github.com/Netflix/vmaf
공식 코드SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
https://github.com/NevSNev/UniST
공식 코드Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
https://github.com/NiFangBaAGe/DATTT
공식 코드Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
https://github.com/NissaFaber/Thesis_repo
공식 코드https://github.com/OFA-Sys/ONE-PEACE
공식 코드ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
https://github.com/OpenGVLab/UniFormerV2
공식 코드UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer
https://github.com/OpenGVLab/VideoMAEv2
공식 코드VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
https://github.com/Orange-OpenSource/AIVC
공식 코드https://github.com/OrdinaryQin/TCA-GCN
공식 코드Skeleton-based Action Recognition via Temporal-Channel Aggregation
https://github.com/PIC4SeRCentre/MPOSE2021_Dataset
공식 코드Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition
https://github.com/PJLab-ADG/DriveArena
공식 코드DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes
https://github.com/PKU-YuanGroup/ConsisID
공식 코드Identity-Preserving Text-to-Video Generation by Frequency Decomposition
https://github.com/PKU-YuanGroup/OpenS2V-Nexus
공식 코드OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
https://github.com/PSCLab-ASU/CSVideoNet
공식 코드CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing
https://github.com/Pachark/SVQ-Forecasting
공식 코드https://github.com/PaddlePaddle/PaddleDetection
공식 코드https://github.com/PaddlePaddle/PaddleGAN
공식 코드https://github.com/PaddlePaddle/PaddleMIX
공식 코드https://github.com/PaddlePaddle/models
공식 코드Deep Concept-wise Temporal Convolutional Networks for Action Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.