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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/UBC-Computer-Vision-Group/DwNet
MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/UKPLab/coling18-multimodalSurvey
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Uemuet/style-transfer
https://github.com/Uemuet/styletransfer-adaptive
https://github.com/VDelv/EEGLearn-Pytorch
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/VQAssessment/FAST-VQA-and-FasterVQA
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
https://github.com/VaanHUANG/CSCI5210HW1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/VainF/pytorch-msssim
https://github.com/Vakihito/SentimentYoutube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Vchitect/Vchitect-2.0
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
https://github.com/ViTAE-Transformer/ViTAE-Transformer
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
https://github.com/ViacheslavBobrov/EEG_RNN_Conv_Learning
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/VideoPRNUExtractor/Weighter
https://github.com/VinACE/trans-vsumm
https://github.com/VincieD/TrafficSign_GAN_Benchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/VishaalMK/VectorDefense
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/VisiumCH/AMLD2020-Dirty-Gancing
https://github.com/VisualComputingInstitute/SiamR-CNN
https://github.com/VisualComputingInstitute/mots_trackingonly_tools
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
https://github.com/VivekUdupa/CPSC8810-DeepLearning_vkoodli_shonnah
https://github.com/Vivswan/ECE-3195-Course-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/WWangYuHsiang/SMILEtrack
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
https://github.com/WangChen0902/BSN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/WangYueFt/dgcnn
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Wangt-CN/DisCo
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
https://github.com/WantingZhao/my_GAT
https://github.com/WaqasSultani/AnomalyDetectionCVPR2018
https://github.com/WarrenGreen/srcnn
https://github.com/WavesUR/embedded_TSM
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/WeLoveKiraboshi/In-PlaneRotation-AwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/WeLoveKiraboshi/In-PlaneRotationAwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Weifeng73/Zero-Shot-Super-resolution
https://github.com/Whiffe/mmaction2_YF
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/WongKinYiu/YOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/WoominM/DeGCN_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.