Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Residual-Guided Learning Representation for Self-Supervised Monocular
  Depth Estimation   Photometric consistency loss is one of the representative objective functions
commonly used for self-supervised monocular depth estimation. However, this
loss often causes unstable depth predictions in textureless or occluded regions
due to incorrect guidance. Recent self-supervised learning approaches tackle
this issue by utilizing feature representations explicitly learned from
auto-encoders, expecting better discriminability than the input image. Despite
the use of auto-encoded features, we observe that the method does not embed
features as discriminative as auto-encoded features. In this paper, we propose
residual guidance loss that enables the depth estimation network to embed the
discriminative feature by transferring the discriminability of auto-encoded
features. We conducted experiments on the KITTI benchmark and verified our
method's superiority and orthogonality on other state-of-the-art methods.

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The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
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The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph