  Attention Mechanism is a widely used method for improving the performance of
convolutional neural networks (CNNs) on computer vision tasks. Despite its
pervasiveness, we have a poor understanding of what its effectiveness stems
from. It is popularly believed that its effectiveness stems from the visual
attention explanation, advocating focusing on the important part of input data
rather than ingesting the entire input. In this paper, we find that there is
only a weak consistency between the attention weights of features and their
importance. Instead, we verify the crucial role of feature map multiplication
in attention mechanism and uncover a fundamental impact of feature map
multiplication on the learned landscapes of CNNs: with the high order
non-linearity brought by the feature map multiplication, it played a
regularization role on CNNs, which made them learn smoother and more stable
landscapes near real samples compared to vanilla CNNs. This smoothness and
stability induce a more predictive and stable behavior in-between real samples,
and make CNNs generate better. Moreover, motivated by the proposed
effectiveness of feature map multiplication, we design feature map
multiplication network (FMMNet) by simply replacing the feature map addition in
ResNet with feature map multiplication. FMMNet outperforms ResNet on various
datasets, and this indicates that feature map multiplication plays a vital role
in improving the performance even without finely designed attention mechanism
in existing methods.
