Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/CoAGCN (1-stream)

CoAGCN (1-stream)

Reported on 16 benchmarks across 8 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

VideoonKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
VideoonKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Temporal Action LocalizationonKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Temporal Action LocalizationonKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action LocalizationonKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action LocalizationonKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Time Series4 results

Action DetectiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action DetectiononKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action RecognitiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action RecognitiononKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Methodology2 results

Zero-Shot LearningonKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Zero-Shot LearningonKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Robots2 results

Activity RecognitiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Activity RecognitiononKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Natural Language Processing2 results

3D Action RecognitiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
33
best: 52.3 (Structured Keypoint Pooling (PPNv2 skeletons+objects))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
3D Action RecognitiononKinetics-Skeleton dataset
GFLOPS per prediction· 2022-03-21
0.18
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009