Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic
  Segmentation   The extensive computational burden limits the usage of CNNs in mobile devices
for dense estimation tasks. In this paper, we present a lightweight network to
address this problem,namely LEDNet, which employs an asymmetric encoder-decoder
architecture for the task of real-time semantic segmentation.More specifically,
the encoder adopts a ResNet as backbone network, where two new operations,
channel split and shuffle, are utilized in each residual block to greatly
reduce computation cost while maintaining higher segmentation accuracy. On the
other hand, an attention pyramid network (APN) is employed in the decoder to
further lighten the entire network complexity. Our model has less than 1M
parameters,and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The
comprehensive experiments demonstrate that our approach achieves
state-of-the-art results in terms of speed and accuracy trade-off on CityScapes
dataset.

9
The categories of this paper are: nlin
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg