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/dlpbc/keras-kinetics-i3d

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

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmis-lab/temporalhead

Knowledge Circuits in Pretrained Transformers

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/arma

Graph Neural Networks with convolutional ARMA filters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc

Simplifying Graph Convolutional Networks

https://github.com/donshen/pointnet.phasedetection

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

https://github.com/doronharitan/human_activity_recognition_LRCN

UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dwtstore/sfm1

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

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/dzwallkilled/IEforAR

Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

https://github.com/ehsannowroozi/RDFS

Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples

https://github.com/eknim/gradcamplusplus

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

https://github.com/eliasboughosn/Spatial-Temporal-Graph-Convolutional-Networks

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/eric-mitchell/serac

Fast Model Editing at Scale

https://github.com/ericksiavichay/cs230-final-project

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/eth212/Qmind_ModelNet10_PointNet

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

https://github.com/ethanluoyc/transporter-pytorch

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition

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

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2

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

https://github.com/fdsa1860/skeletal

https://github.com/firework8/ProtoGCN

Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/frankaging/pyvene

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

https://github.com/freddieee/pn_6d_single

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

https://github.com/frgfm/torch-cam

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

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