https://github.com/zwx8981/DBCNN-PyTorch
공식 코드Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zwxu064/RANP
공식 코드RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
https://github.com/zyj-2000/thqa
공식 코드THQA: A Perceptual Quality Assessment Database for Talking Heads
https://github.com/zympsyche/bevworld
공식 코드BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space
https://github.com/zyoungxu/garamost
공식 코드https://github.com/zyxelsa/motioncrafter
공식 코드MotionCrafter: One-Shot Motion Customization of Diffusion Models
https://github.com/zzc-1998/ddh-qa
공식 코드DDH-QA: A Dynamic Digital Humans Quality Assessment Database
https://github.com/zzc-1998/md-vqa
공식 코드MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
https://github.com/zzc-1998/vqa_pc
공식 코드Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric
https://github.com/zzh-tech/interpany-clearer
공식 코드Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation
https://github.com/zzhao71/ripplecot
공식 코드https://github.com/zzy-zhou/dsec-mos
공식 코드Event-Free Moving Object Segmentation from Moving Ego Vehicle
https://github.com/zzysteve/skeletonx
공식 코드SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
https://gitlab.com/MISLgit/forensic-similarity-for-digital-images
공식 코드https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos
공식 코드FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://gitlab.com/nct_tso_public/pitfalls_bn
공식 코드https://gitlab.com/nct_tso_public/surgical_skill_classification
공식 코드Video-based surgical skill assessment using 3D convolutional neural networks
https://gitlab.com/sbonnefoy/siamese_net_change_detection
공식 코드https://gitlab.kitware.com/darpa_learn/learn
공식 코드LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning
https://huggingface.co/datasets/SoftMINER-Group/NicheHazardQA
공식 코드Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models
https://huggingface.co/datasets/SoftMINER-Group/TechHazardQA
공식 코드https://gitee.com/gai-shaoyan/mind3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://gitee.com/zhengkeli/zkl-knowedit-rome
https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3
https://github.com/15517965908/keras-DBPN
https://github.com/15534081591/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/1zgh/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/20151001860/Real_ESRGAN_paddle
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/2020-sk49/VFOST
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/2023-MindSpore-1/ms-code-187
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-192
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.