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/ftdlyc/pointnet_pytorch

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

https://github.com/ftorres11/saliencysense

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

https://github.com/fxia22/pointnet.pytorch

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

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/gd-goblin/C-3PO_Motion_Retargeting_Module

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/gd-goblin/C-3PO_RobotControlModule

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

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

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/gmum/umwpl2021

Graph Attention Networks

https://github.com/google-deepmind/kinetics-i3d

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

https://github.com/google-research/google-research/tree/master/poem

View-Invariant Probabilistic Embedding for Human Pose

https://github.com/gordicaleksa/pytorch-GAT

Graph Attention Networks

https://github.com/handasontam/GAT-with-edgewise-attention

Graph Attention Networks

https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/hassony2/kinetics_i3d_pytorch

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

https://github.com/hazdzz/ChebyNet

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

https://github.com/hazdzz/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/hazdzz/SGC

Simplifying Graph Convolutional Networks

https://github.com/hehefan/P4Transformer

https://github.com/hehefan/PSTNet2

https://github.com/helloxy96/CS5242_Project2020

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

https://github.com/hhe-distance/AIF-CNN

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

https://github.com/hi-archers/stableke

MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

https://github.com/highwaywu/imageforensicsosn

https://github.com/hiyouga/fastedit

Locating and Editing Factual Associations in GPT

https://github.com/hnVfly/pointnet.mxnet

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

https://github.com/hnmspirit/pointnet1

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

https://github.com/huguyuehuhu/HCN-pytorch

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

https://github.com/hyperparameters/tracking_via_colorization

Tracking Emerges by Colorizing Videos

https://github.com/hz-ants/pointnet.pytorch

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

https://github.com/iamjeff7/j-va-aagcn

View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition

https://github.com/if-loops/adaptive-selective-synaptic-dampening

Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening

https://github.com/imanlab/deep_movement_primitives

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

https://github.com/isotlaboratory/ml4vrp

Graph Attention Networks

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