  The attention mechanisms have been employed in Convolutional Neural Network
(CNN) to enhance the feature representation. However, existing attention
mechanisms only concentrate on refining the features inside each sample and
neglect the discrimination between different samples. In this paper, we propose
a batch aware attention module (BA2M) for feature enrichment from a distinctive
perspective. More specifically, we first get the sample-wise attention
representation (SAR) by fusing the channel, local spatial and global spatial
attention maps within each sample. Then, we feed the SARs of the whole batch to
a normalization function to get the weights for each sample. The weights serve
to distinguish the features' importance between samples in a training batch
with different complexity of content. The BA2M could be embedded into different
parts of CNN and optimized with the network in an end-to-end manner. The design
of BA2M is lightweight with few extra parameters and calculations. We validate
BA2M through extensive experiments on CIFAR-100 and ImageNet-1K for the image
recognition task. The results show that BA2M can boost the performance of
various network architectures and outperforms many classical attention methods.
Besides, BA2M exceeds traditional methods of re-weighting samples based on the
loss value.
