Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human
  Pose Estimation   A well-designed strong-weak augmentation strategy and the stable teacher to
generate reliable pseudo labels are essential in the teacher-student framework
of semi-supervised learning (SSL). Considering these in mind, to suit the
semi-supervised human pose estimation (SSHPE) task, we propose a novel approach
referred to as Pose-MUM that modifies Mix/UnMix (MUM) augmentation. Like MUM in
the dense prediction task, the proposed Pose-MUM makes strong-weak augmentation
for pose estimation and leads the network to learn the relationship between
each human key point much better than the conventional methods by adding the
mixing process in intermediate layers in a stochastic manner. In addition, we
employ the exponential-moving-average-normalization (EMAN) teacher, which is
stable and well-suited to the SSL framework and furthermore boosts the
performance. Extensive experiments on MS-COCO dataset show the superiority of
our proposed method by consistently improving the performance over the previous
methods following SSHPE benchmark.

31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int