Papers with Code 아카이브

3D Action Recognition

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

3

보존된 평가 기록

13

코드

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

https://github.com/KiranAkadas/My_Pointnet_v2

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

https://github.com/KrisLee512/ST-GCN

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Kristof-Neys/GAT_Neo4j_DGL

Graph Attention Networks

https://github.com/LONG-9621/Extract_Point_3D

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

https://github.com/LONG-9621/PointNet

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

https://github.com/LarryJiang134/Image_manipulation_detection

Learning Rich Features for Image Manipulation Detection

https://github.com/LebronGG/PointNet

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

https://github.com/LouisDumont/GCN---re-implementation

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/LukasHedegaard/co3d

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

https://github.com/Lw510107/pointnet-2018.6.27-

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

https://github.com/MarkoLewis-Projects/Sign_language_detection

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

https://github.com/Maxchengqin/DSCNet

https://github.com/Maxchengqin/MSSTNet_2D_3D

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Graph Attention Networks

https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification

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

https://github.com/Messham87/COVID_GAT

Graph Attention Networks

https://github.com/Mind23-2/MindCode-4/tree/main/gat

Graph Attention Networks

https://github.com/Mind23-2/MindCode-63

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

https://github.com/MindSpore-paper-code-2/code3/tree/main/stgcn

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

https://github.com/MindSpore-paper-code-3/code10/tree/main/flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/MindSpore-paper-code-3/code4/tree/main/flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/MindSpore-paper-code-3/code7/tree/main/flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet

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

https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2

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

https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU

Graph Attention Networks

https://github.com/ModelBunker/PointNet-TensorFlow

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

https://github.com/Moon-Xuezha/PointNet

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

https://github.com/NVIDIA/flownet2-pytorch

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/Negus25/GAT-Mindspore

Graph Attention Networks

https://github.com/OanaIgnat/i3d_keras

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

https://github.com/PPPrior/i3d-pytorch

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

https://github.com/PaParaZz1/PointNet

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

https://github.com/PaParaZz1/TemporalShiftModule

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/PeterWang512/FALdetector

Detecting Photoshopped Faces by Scripting Photoshop

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