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Models/ST-CNN

ST-CNN

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Computer Vision8 results

Action LocalizationonGTEA
Acc· 2016-02-09
60.6
best: 89.8 (Semantic2Graph)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action LocalizationonGTEA
F1@10%· 2016-02-09
58.7
best: 96.1 (FACT)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action LocalizationonGTEA
F1@25%· 2016-02-09
54.4
best: 95.6 (FACT)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action LocalizationonGTEA
F1@50%· 2016-02-09
41.9
best: 91.3 (Semantic2Graph)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action SegmentationonGTEA
Acc· 2016-02-09
60.6
best: 89.8 (Semantic2Graph)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action SegmentationonGTEA
F1@10%· 2016-02-09
58.7
best: 96.1 (FACT)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action SegmentationonGTEA
F1@25%· 2016-02-09
54.4
best: 95.6 (FACT)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995
Action SegmentationonGTEA
F1@50%· 2016-02-09
41.9
best: 91.3 (Semantic2Graph)
SOTA
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation arXiv:1602.02995