Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 15 Keypoints Is All You Need   Pose tracking is an important problem that requires identifying unique human
pose-instances and matching them temporally across different frames of a video.
However, existing pose tracking methods are unable to accurately model temporal
relationships and require significant computation, often computing the tracks
offline. We present an efficient Multi-person Pose Tracking method, KeyTrack,
that only relies on keypoint information without using any RGB or optical flow
information to track human keypoints in real-time. Keypoints are tracked using
our Pose Entailment method, in which, first, a pair of pose estimates is
sampled from different frames in a video and tokenized. Then, a
Transformer-based network makes a binary classification as to whether one pose
temporally follows another. Furthermore, we improve our top-down pose
estimation method with a novel, parameter-free, keypoint refinement technique
that improves the keypoint estimates used during the Pose Entailment step. We
achieve state-of-the-art results on the PoseTrack'17 and the PoseTrack'18
benchmarks while using only a fraction of the computation required by most
other methods for computing the tracking information.

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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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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: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: physics