Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PointTrack++ for Effective Online Multi-Object Tracking and Segmentation   Multiple-object tracking and segmentation (MOTS) is a novel computer vision
task that aims to jointly perform multiple object tracking (MOT) and instance
segmentation. In this work, we present PointTrack++, an effective on-line
framework for MOTS, which remarkably extends our recently proposed PointTrack
framework. To begin with, PointTrack adopts an efficient one-stage framework
for instance segmentation, and learns instance embeddings by converting compact
image representations to un-ordered 2D point cloud. Compared with PointTrack,
our proposed PointTrack++ offers three major improvements. Firstly, in the
instance segmentation stage, we adopt a semantic segmentation decoder trained
with focal loss to improve the instance selection quality. Secondly, to further
boost the segmentation performance, we propose a data augmentation strategy by
copy-and-paste instances into training images. Finally, we introduce a better
training strategy in the instance association stage to improve the
distinguishability of learned instance embeddings. The resulting framework
achieves the state-of-the-art performance on the 5th BMTT MOTChallenge.

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The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
3
The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int