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Table 9 Comparison of different methods on their respective datasets

From: A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition

  Proposed method (%) Method proposed in [15] (ConvNets+LSTM) + pooling fusion (%) Method proposed in [16] (DT + temporal enhanced features) + Fisher vector (%)
Average pooling Maximum pooling
Vision 79.2 68.5% 75.0 78.4
Sensors 43.1 49.5 69.0
Fusion 85.4 76.5 80.5 83.7
  1. Proposed method was applied to the eButton datasets described in Section 3.1; the other two methods were applied to the datasets described in [16]