Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparative Study of High-Recall Real-Time Semantic Segmentation Based
  on Swift Factorized Network   Semantic Segmentation (SS) is the task to assign a semantic label to each
pixel of the observed images, which is of crucial significance for autonomous
vehicles, navigation assistance systems for the visually impaired, and
augmented reality devices. However, there is still a long way for SS to be put
into practice as there are two essential challenges that need to be addressed:
efficiency and evaluation criterions for practical application. For specific
application scenarios, different criterions need to be adopted. Recall rate is
an important criterion for many tasks like autonomous vehicles. For autonomous
vehicles, we need to focus on the detection of the traffic objects like cars,
buses, and pedestrians, which should be detected with high recall rates. In
other words, it is preferable to detect it wrongly than miss it, because the
other traffic objects will be dangerous if the algorithm miss them and segment
them as safe roadways. In this paper, our main goal is to explore possible
methods to attain high recall rate. Firstly, we propose a real-time SS network
named Swift Factorized Network (SFN). The proposed network is adapted from
SwiftNet, whose structure is a typical U-shape structure with lateral
connections. Inspired by ERFNet and Global convolution Networks (GCNet), we
propose two different blocks to enlarge valid receptive field. They do not take
up too much calculation resources, but significantly enhance the performance
compared with the baseline network. Secondly, we explore three ways to achieve
higher recall rate, i.e. loss function, classifier and decision rules. We
perform a comprehensive set of experiments on state-of-the-art datasets
including CamVid and Cityscapes. We demonstrate that our SS convolutional
neural networks reach excellent performance. Furthermore, we make a detailed
analysis and comparison of the three proposed methods on the promotion of
recall rate.

11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat