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Models/CoST-GCN* (1-stream)

CoST-GCN* (1-stream)

Reported on 40 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 Vision15 results

VideoonNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 92.2 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
VideoonNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 90.9 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
VideoonNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
VideoonKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Temporal Action LocalizationonNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 92.2 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Temporal Action LocalizationonNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 90.9 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Temporal Action LocalizationonNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Temporal Action LocalizationonKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action LocalizationonNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 92.2 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action LocalizationonNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 90.9 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action LocalizationonNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action LocalizationonKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Time Series10 results

Action DetectiononNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 92.2 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action DetectiononNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 90.9 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action DetectiononNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action DetectiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action RecognitiononNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 96.7 (DSCNet (RGB + Pose))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action RecognitiononNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 95.6 (DSCNet (RGB + Pose))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action RecognitiononNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Action RecognitiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Methodology5 results

Zero-Shot LearningonNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 92.2 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Zero-Shot LearningonNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 90.9 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Zero-Shot LearningonNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Zero-Shot LearningonKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Robots5 results

Activity RecognitiononNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 96.7 (DSCNet (RGB + Pose))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Activity RecognitiononNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 95.6 (DSCNet (RGB + Pose))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Activity RecognitiononNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
Activity RecognitiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009

Natural Language Processing5 results

3D Action RecognitiononNTU RGB+D 120
Accuracy (Cross-Setup)· 2022-03-21
81.6
best: 92.2 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
3D Action RecognitiononNTU RGB+D 120
Accuracy (Cross-Subject)· 2022-03-21
79.4
best: 90.9 (ProtoGCN)
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
3D Action RecognitiononNTU RGB+D 120
GFLOPS per prediction· 2022-03-21
0.16
best: 37.38 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009
3D Action RecognitiononKinetics-Skeleton dataset
Accuracy· 2022-03-21
30.2
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
0.11
best: 26.91 (AGCN (2-stream))
Continual Spatio-Temporal Graph Convolutional Networks arXiv:2203.11009