Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks   Most existing crowd counting systems rely on the availability of the object
location annotation which can be expensive to obtain. To reduce the annotation
cost, one attractive solution is to leverage a large number of unlabeled images
to build a crowd counting model in semi-supervised fashion. This paper tackles
the semi-supervised crowd counting problem from the perspective of feature
learning. Our key idea is to leverage the unlabeled images to train a generic
feature extractor rather than the entire network of a crowd counter. The
rationale of this design is that learning the feature extractor can be more
reliable and robust towards the inevitable noisy supervision generated from the
unlabeled data. Also, on top of a good feature extractor, it is possible to
build a density map regressor with much fewer density map annotations.
Specifically, we proposed a novel semi-supervised crowd counting method which
is built upon two innovative components: (1) a set of inter-related binary
segmentation tasks are derived from the original density map regression task as
the surrogate prediction target; (2) the surrogate target predictors are
learned from both labeled and unlabeled data by utilizing a proposed
self-training scheme which fully exploits the underlying constraints of these
binary segmentation tasks. Through experiments, we show that the proposed
method is superior over the existing semisupervised crowd counting method and
other representative baselines.

23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph