  Attention mechanism, being frequently used to train networks for better
feature representations, can effectively disentangle the target object from
irrelevant objects in the background. Given an arbitrary image, we find that
the background's irrelevant objects are most likely to occlude/block the target
object. We propose, based on this finding, a ReplaceBlock to simulate the
situations when the target object is partially occluded by the objects that are
deemed as background. Specifically, ReplaceBlock erases the target object in
the image, and then generates a feature map with only irrelevant objects and
background by the model. Finally, some regions in the background feature map
are used to replace some regions of the target object in the original image
feature map. In this way, ReplaceBlock can effectively simulate the feature map
of the occluded image. The experimental results show that ReplaceBlock works
better than DropBlock in regularizing convolutional networks.
