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AMNet: Memorability Estimation with Attention

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Figure

Figure 1:AMNet iteratively generates attention mapslinked to the image regions correlated with the memorabil-ity
Figure 2: A pretrained RestNet50 (of three stepsa) is followed by the soft attention mechanism (b) with LSTM (c), which over a sequence T = 3 produces attention maps, each conditioned on the previous LSTM state ht−1 and the entire imagefeature vector x
Figure 3: Training/validation losses and memorability rankcorrelation on the validation dataset in the LaMem split1.
Table 1: Average Spearman’s rank correlation ρ and MSEover 5 test splits of the LaMem dataset.
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