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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DBPN

Deep Back-Projection Networks For Super-Resolution

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/ESRGAN

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/gan

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/OSVOS

One-Shot Video Object Segmentation

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/predrnn%2B%2B

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ReIDStrongBaseline

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/r2plus1d

A Closer Look at Spatiotemporal Convolutions for Action Recognition

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet3d

Revisiting 3D ResNets for Video Recognition

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stgcn

DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stnet

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tgcn

TGCN: Time Domain Graph Convolutional Network for Multiple Objects Tracking

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tracktor

Tracking without bells and whistles

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tracktor%2B%2B

Tracking without bells and whistles

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tsm

TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device

https://github.com/2023-MindSpore-1/ms-code-218/tree/main/ocean_model

Ocean: Object-aware Anchor-free Tracking

https://github.com/2023-MindSpore-1/ms-code-23

Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample

https://github.com/2023-MindSpore-1/ms-code-24

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/2023-MindSpore-1/ms-code-28

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

https://github.com/2023-MindSpore-1/ms-code-35

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/2023-MindSpore-1/ms-code-49

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/2023-MindSpore-1/ms-code-5/tree/main/gpt2

GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization

https://github.com/2023-MindSpore-1/ms-code-65

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/2023-MindSpore-1/ms-code-68

A Closer Look at Spatiotemporal Convolutions for Action Recognition

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TCN

Time-Contrastive Networks: Self-Supervised Learning from Video

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tracktor%2B%2B

Tracking without bells and whistles

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn

Temporal Relational Reasoning in Videos

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsm

TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsn

Temporal Segment Networks for Action Recognition in Videos

https://github.com/2023-MindSpore-4/Code-5/tree/main/MVD

Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning

https://github.com/2023-MindSpore-4/Code10/tree/main/VGG

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv5

PP-YOLOE: An evolved version of YOLO

https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/2023-MindSpore-4/Code10/tree/main/glore_res

Graph-Based Global Reasoning Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.