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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/MicroprocessorX069/Comparison-of-DC-GANS-and-SA-GANS
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/MikeG112/RFSBD
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/MikelBros/Yolov3_DeepSort_Pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/Mikkil5112/PredNet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/Mind23-2/MindCode-101/tree/main/MVD
https://github.com/Mind23-2/MindCode-101/tree/main/Non_local
https://github.com/Mind23-2/MindCode-101/tree/main/nima
https://github.com/Mind23-2/MindCode-11
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/Mind23-2/MindCode-119
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-12
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
https://github.com/Mind23-2/MindCode-129
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/Mind23-2/MindCode-130
https://github.com/Mind23-2/MindCode-3/tree/main/I3D
https://github.com/Mind23-2/MindCode-3/tree/main/MVD
https://github.com/Mind23-2/MindCode-3/tree/main/nima
https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-33
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/Mind23-2/MindCode-4/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-42
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/Mind23-2/MindCode-49
https://github.com/Mind23-2/MindCode-5/tree/main/OSVOS
https://github.com/Mind23-2/MindCode-5/tree/main/predrnn%2B%2B
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
https://github.com/Mind23-2/MindCode-5/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/Mind23-2/MindCode-50
https://github.com/Mind23-2/MindCode-62
Video Classification with Channel-Separated Convolutional Networks
https://github.com/Mind23-2/MindCode-63
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Mind23-2/MindCode-68
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/Mind23-2/MindCode-74
https://github.com/Mind23-2/MindCode-88/tree/main/slowfast
https://github.com/MindCode-4/code-1/tree/main/tvlt
https://github.com/MindCode-4/code-1/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindCode-4/code-11/tree/main/ACGNet
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
https://github.com/MindCode-4/code-11/tree/main/delving-deeper-into-convolutional
Delving Deeper into Convolutional Networks for Learning Video Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.