Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do Neural Networks for Segmentation Understand Insideness?   The insideness problem is an aspect of image segmentation that consists of
determining which pixels are inside and outside a region. Deep Neural Networks
(DNNs) excel in segmentation benchmarks, but it is unclear if they have the
ability to solve the insideness problem as it requires evaluating long-range
spatial dependencies. In this paper, the insideness problem is analysed in
isolation, without texture or semantic cues, such that other aspects of
segmentation do not interfere in the analysis. We demonstrate that DNNs for
segmentation with few units have sufficient complexity to solve insideness for
any curve. Yet, such DNNs have severe problems with learning general solutions.
Only recurrent networks trained with small images learn solutions that
generalize well to almost any curve. Recurrent networks can decompose the
evaluation of long-range dependencies into a sequence of local operations, and
learning with small images alleviates the common difficulties of training
recurrent networks with a large number of unrolling steps.

8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph