Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RPT: Learning Point Set Representation for Siamese Visual Tracking   While remarkable progress has been made in robust visual tracking, accurate
target state estimation still remains a highly challenging problem. In this
paper, we argue that this issue is closely related to the prevalent bounding
box representation, which provides only a coarse spatial extent of object. Thus
an effcient visual tracking framework is proposed to accurately estimate the
target state with a finer representation as a set of representative points. The
point set is trained to indicate the semantically and geometrically significant
positions of target region, enabling more fine-grained localization and
modeling of object appearance. We further propose a multi-level aggregation
strategy to obtain detailed structure information by fusing hierarchical
convolution layers. Extensive experiments on several challenging benchmarks
including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method
achieves new state-of-the-art performance while running at over 20 FPS.

7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an