Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GSVNet: Guided Spatially-Varying Convolution for Fast Semantic
  Segmentation on Video   This paper addresses fast semantic segmentation on video.Video segmentation
often calls for real-time, or even fasterthan real-time, processing. One common
recipe for conserving computation arising from feature extraction is to
propagate features of few selected keyframes. However, recent advances in fast
image segmentation make these solutions less attractive. To leverage fast image
segmentation for furthering video segmentation, we propose a simple yet
efficient propagation framework. Specifically, we perform lightweight flow
estimation in 1/8-downscaled image space for temporal warping in segmentation
outpace space. Moreover, we introduce a guided spatially-varying convolution
for fusing segmentations derived from the previous and current frames, to
mitigate propagation error and enable lightweight feature extraction on
non-keyframes. Experimental results on Cityscapes and CamVid show that our
scheme achieves the state-of-the-art accuracy-throughput trade-off on video
segmentation.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga