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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific/code-10/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-10/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-11/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
INR-V: A Continuous Representation Space for Video-based Generative Tasks
https://github.com/MindSpore-scientific/code-11/tree/main/mgan-mindspore
MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/MindSpore-scientific/code-11/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/MindSpore-scientific/code-11/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-12/tree/main/AVA_hpa
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
https://github.com/MindSpore-scientific/code-13/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/MindSpore-scientific/code-13/tree/main/token_learner
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindSpore-scientific/code-14/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific/code-14/tree/main/vat
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
https://github.com/MindSpore-scientific/code-2/tree/main/stable-sam
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
https://github.com/MindSpore-scientific/code-2/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific/code-3/tree/main/ACGNet
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
https://github.com/MindSpore-scientific/code-4/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-7/tree/main/SAM
https://github.com/MindSpore-scientific/code-7/tree/main/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindSpore-scientific/code-7/tree/main/X_CLIP
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/MindSpore-scientific/code-7/tree/main/dino
https://github.com/MindSpore-scientific/code-8/tree/main/mgan-mindspore
MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/MindSpore-scientific/code-8/tree/main/pytorch_convgru
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
https://github.com/MindSpore-scientific/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-9/tree/main/Zero-DCE
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network
https://github.com/MisaOgura/flashtorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MkuuWaUjinga/Self-Supervised-Learning-for-Tracktor
https://github.com/ModelBunker/PointNet-TensorFlow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MohamedAliRashad/Crystal
https://github.com/MohamedNagyMostafa/RobMOT
https://github.com/MohammadRezaQaderi/Video-Swin-Transformer
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Mohanned-Elkholy/ResNet-GAN
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Mohitkr95/Image-Super-Resolution
https://github.com/Moon-Xuezha/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Mosasaur5526/BCM-iCT-torch
https://github.com/MrCat9/Sklearn_Note
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.