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

ReplaceBlock: An improved regularization method based on background information