Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bootstrapped Representation Learning for Skeleton-Based Action
  Recognition   In this work, we study self-supervised representation learning for 3D
skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL)
for representation learning on skeleton sequence data and propose a new data
augmentation strategy including two asymmetric transformation pipelines. We
also introduce a multi-viewpoint sampling method that leverages multiple
viewing angles of the same action captured by different cameras. In the
semi-supervised setting, we show that the performance can be further improved
by knowledge distillation from wider networks, leveraging once more the
unlabeled samples. We conduct extensive experiments on the NTU-60 and NTU-120
datasets to demonstrate the performance of our proposed method. Our method
consistently outperforms the current state of the art on both linear evaluation
and semi-supervised benchmarks.

37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph