Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How to Train Your Dragon: Tamed Warping Network for Semantic Video
  Segmentation   Real-time semantic segmentation on high-resolution videos is challenging due
to the strict requirements of speed. Recent approaches have utilized the
inter-frame continuity to reduce redundant computation by warping the feature
maps across adjacent frames, greatly speeding up the inference phase. However,
their accuracy drops significantly owing to the imprecise motion estimation and
error accumulation. In this paper, we propose to introduce a simple and
effective correction stage right after the warping stage to form a framework
named Tamed Warping Network (TWNet), aiming to improve the accuracy and
robustness of warping-based models. The experimental results on the Cityscapes
dataset show that with the correction, the accuracy (mIoU) significantly
increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8
FPS. For non-rigid categories such as "human" and "object", the improvements of
IoU are even higher than 18 percentage points.

31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph