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Models/AMeFu-Net

AMeFu-Net

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

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

Robots3 results

Activity RecognitiononHMDB51
1:1 Accuracy· 2020-10-20
75.5
best: 77.3 (STRM)
SOTA
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition arXiv:2010.09982
Activity RecognitiononKinetics-100
Accuracy· 2020-10-20
86.8
best: 94.7 (Name Tuning)
SOTA
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition arXiv:2010.09982
Activity RecognitiononUCF101
1:1 Accuracy· 2020-10-20
95.5
best: 96.8 (STRM)
SOTA
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition arXiv:2010.09982

Time Series3 results

Action RecognitiononHMDB51
1:1 Accuracy· 2020-10-20
75.5
best: 77.3 (STRM)
SOTA
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition arXiv:2010.09982
Action RecognitiononKinetics-100
Accuracy· 2020-10-20
86.8
best: 94.7 (Name Tuning)
SOTA
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition arXiv:2010.09982
Action RecognitiononUCF101
1:1 Accuracy· 2020-10-20
95.5
best: 96.8 (STRM)
SOTA
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition arXiv:2010.09982