Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distance transform regression for spatially-aware deep semantic
  segmentation   Understanding visual scenes relies more and more on dense pixel-wise
classification obtained via deep fully convolutional neural networks. However,
due to the nature of the networks, predictions often suffer from blurry
boundaries and ill-segmented shapes, fueling the need for post-processing. This
work introduces a new semantic segmentation regularization based on the
regression of a distance transform. After computing the distance transform on
the label masks, we train a FCN in a multi-task setting in both discrete and
continuous spaces by learning jointly classification and distance regression.
This requires almost no modification of the network structure and adds a very
low overhead to the training process. Learning to approximate the distance
transform back-propagates spatial cues that implicitly regularizes the
segmentation. We validate this technique with several architectures on various
datasets, and we show significant improvements compared to competitive
baselines.

35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th