Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: S3-Net: A Fast and Lightweight Video Scene Understanding Network by
  Single-shot Segmentation   Real-time understanding in video is crucial in various AI applications such
as autonomous driving. This work presents a fast single-shot segmentation
strategy for video scene understanding. The proposed net, called S3-Net,
quickly locates and segments target sub-scenes, meanwhile extracts structured
time-series semantic features as inputs to an LSTM-based spatio-temporal model.
Utilizing tensorization and quantization techniques, S3-Net is intended to be
lightweight for edge computing. Experiments using CityScapes, UCF11, HMDB51 and
MOMENTS datasets demonstrate that the proposed S3-Net achieves an accuracy
improvement of 8.1% versus the 3D-CNN based approach on UCF11, a storage
reduction of 6.9x and an inference speed of 22.8 FPS on CityScapes with a
GTX1080Ti GPU.

29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th