  Attention networks have successfully boosted the performance in various
vision problems. Previous works lay emphasis on designing a new attention
module and individually plug them into the networks. Our paper proposes a
novel-and-simple framework that shares an attention module throughout different
network layers to encourage the integration of layer-wise information and this
parameter-sharing module is referred as Dense-and-Implicit-Attention (DIA)
unit. Many choices of modules can be used in the DIA unit. Since Long Short
Term Memory (LSTM) has a capacity of capturing long-distance dependency, we
focus on the case when the DIA unit is the modified LSTM (refer as DIA-LSTM).
Experiments on benchmark datasets show that the DIA-LSTM unit is capable of
emphasizing layer-wise feature interrelation and leads to significant
improvement of image classification accuracy. We further empirically show that
the DIA-LSTM has a strong regularization ability on stabilizing the training of
deep networks by the experiments with the removal of skip connections or Batch
Normalization in the whole residual network. The code is released at
https://github.com/gbup-group/DIANet.
