Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ReplaceBlock: An improved regularization method based on background
  information   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.

21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess