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Models/MS-TCN++ (I3D)

MS-TCN++ (I3D)

Reported on 12 benchmarks across 2 tasks · 1 paper

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

Computer Vision12 results

Action LocalizationonBreakfast
Acc· 2020-06-16
67.6
best: 78 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action LocalizationonBreakfast
Average F1· 2020-06-16
56.2
best: 76.2 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action LocalizationonBreakfast
Edit· 2020-06-16
65.6
best: 79.7 (FACT (efficient hybrid of convolution and transformer model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action LocalizationonBreakfast
F1@10%· 2020-06-16
64.1
best: 82.1 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action LocalizationonBreakfast
F1@25%· 2020-06-16
58.6
best: 79 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action LocalizationonBreakfast
F1@50%· 2020-06-16
45.9
best: 67.5 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action SegmentationonBreakfast
Acc· 2020-06-16
67.6
best: 78 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action SegmentationonBreakfast
Average F1· 2020-06-16
56.2
best: 76.2 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action SegmentationonBreakfast
Edit· 2020-06-16
65.6
best: 79.7 (FACT (efficient hybrid of convolution and transformer model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action SegmentationonBreakfast
F1@10%· 2020-06-16
64.1
best: 82.1 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action SegmentationonBreakfast
F1@25%· 2020-06-16
58.6
best: 79 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220
Action SegmentationonBreakfast
F1@50%· 2020-06-16
45.9
best: 67.5 (AdaFocus (newly extracted I3D-features, LT-Context model))
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation arXiv:2006.09220