Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Multi-Object Tracking with Cross-Input Consistency   In this paper, we propose a self-supervised learning procedure for training a
robust multi-object tracking (MOT) model given only unlabeled video. While
several self-supervisory learning signals have been proposed in prior work on
single-object tracking, such as color propagation and cycle-consistency, these
signals cannot be directly applied for training RNN models, which are needed to
achieve accurate MOT: they yield degenerate models that, for instance, always
match new detections to tracks with the closest initial detections. We propose
a novel self-supervisory signal that we call cross-input consistency: we
construct two distinct inputs for the same sequence of video, by hiding
different information about the sequence in each input. We then compute tracks
in that sequence by applying an RNN model independently on each input, and
train the model to produce consistent tracks across the two inputs. We evaluate
our unsupervised method on MOT17 and KITTI -- remarkably, we find that, despite
training only on unlabeled video, our unsupervised approach outperforms four
supervised methods published in the last 1--2 years, including Tracktor++,
FAMNet, GSM, and mmMOT.

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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys