Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interactive Video Object Segmentation in the Wild   In this paper we present our system for human-in-the-loop video object
segmentation. The backbone of our system is a method for one-shot video object
segmentation. While fast, this method requires an accurate pixel-level
segmentation of one (or several) frames as input. As manually annotating such a
segmentation is impractical, we propose a deep interactive image segmentation
method, that can accurately segment objects with only a handful of clicks. On
the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on
average, setting the new state of the art. Furthermore, as our method
iteratively refines an initial segmentation, it can effectively correct frames
where the video object segmentation fails, thus allowing users to quickly
obtain high quality results even on challenging sequences. Finally, we
investigate usage patterns and give insights in how many steps users take to
annotate frames, what kind of corrections they provide, etc., thus giving
important insights for further improving interactive video segmentation.

37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
19
The categories of this paper are: eess
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an