Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint
  Localization   Localizing keypoints of an object is a basic visual problem. However,
supervised learning of a keypoint localization network often requires a large
amount of data, which is expensive and time-consuming to obtain. To remedy
this, there is an ever-growing interest in semi-supervised learning (SSL),
which leverages a small set of labeled data along with a large set of unlabeled
data. Among these SSL approaches, pseudo-labeling (PL) is one of the most
popular. PL approaches apply pseudo-labels to unlabeled data, and then train
the model with a combination of the labeled and pseudo-labeled data
iteratively. The key to the success of PL is the selection of high-quality
pseudo-labeled samples. Previous works mostly select training samples by
manually setting a single confidence threshold. We propose to automatically
select reliable pseudo-labeled samples with a series of dynamic thresholds,
which constitutes a learning curriculum. Extensive experiments on six keypoint
localization benchmark datasets demonstrate that the proposed approach
significantly outperforms the previous state-of-the-art SSL approaches.

13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an