Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via
  Cost Volume Aware Regularizer   Recent end-to-end deep neural networks for disparity regression have achieved
the state-of-the-art performance. However, many well-acknowledged specific
properties of disparity estimation are omitted in these deep learning
algorithms. Especially, matching cost volume, one of the most important
procedure, is treated as a normal intermediate feature for the following
softargmin regression, lacking explicit constraints compared with those
traditional algorithms. In this paper, inspired by previous canonical
definition of cost volume, we propose the noise-sampling cross entropy loss
function to regularize the cost volume produced by deep neural networks to be
unimodal and coherent. Extensive experiments validate that the proposed
noise-sampling cross entropy loss can not only help neural networks learn more
informative cost volume, but also lead to better stereo matching performance
compared with several representative algorithms.

31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
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The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ