Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Object Tracking with Cycle-consistent Siamese Networks   Self-supervised learning for visual object tracking possesses valuable
advantages compared to supervised learning, such as the non-necessity of
laborious human annotations and online training. In this work, we exploit an
end-to-end Siamese network in a cycle-consistent self-supervised framework for
object tracking. Self-supervision can be performed by taking advantage of the
cycle consistency in the forward and backward tracking. To better leverage the
end-to-end learning of deep networks, we propose to integrate a Siamese region
proposal and mask regression network in our tracking framework so that a fast
and more accurate tracker can be learned without the annotation of each frame.
The experiments on the VOT dataset for visual object tracking and on the DAVIS
dataset for video object segmentation propagation show that our method
outperforms prior approaches on both tasks.

0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin