Figure 4.

Architecture of DeepCINAC neural network. As a first step, for each set of inputs of the same cell, we extract CNNs features of video frames that we pass to an attention mechanism and feed the outputs into a forward pass network (FU, green units) and a backward pass network (BU, orange units), representing a bidirectional LSTM. Another bidirectional LSTM is fed from the attention mechanism and previous bidirectional LSTM outputs. A LSTM (MU, blue units) then integrates the outputs from the process of the three types of inputs to generate a final video representation. A sigmoid activation function is finally used to produce a probability for the cell to be active at each given frame given as input.