Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional
  Variational Autoencoders   In this paper, we propose the first framework (UCNet) to employ uncertainty
for RGB-D saliency detection by learning from the data labeling process.
Existing RGB-D saliency detection methods treat the saliency detection task as
a point estimation problem, and produce a single saliency map following a
deterministic learning pipeline. Inspired by the saliency data labeling
process, we propose probabilistic RGB-D saliency detection network via
conditional variational autoencoders to model human annotation uncertainty and
generate multiple saliency maps for each input image by sampling in the latent
space. With the proposed saliency consensus process, we are able to generate an
accurate saliency map based on these multiple predictions. Quantitative and
qualitative evaluations on six challenging benchmark datasets against 18
competing algorithms demonstrate the effectiveness of our approach in learning
the distribution of saliency maps, leading to a new state-of-the-art in RGB-D
saliency detection.

27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph