Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Deep Domain Adaptation for Pedestrian Detection   This paper addresses the problem of unsupervised domain adaptation on the
task of pedestrian detection in crowded scenes. First, we utilize an iterative
algorithm to iteratively select and auto-annotate positive pedestrian samples
with high confidence as the training samples for the target domain. Meanwhile,
we also reuse negative samples from the source domain to compensate for the
imbalance between the amount of positive samples and negative samples. Second,
based on the deep network we also design an unsupervised regularizer to
mitigate influence from data noise. More specifically, we transform the last
fully connected layer into two sub-layers - an element-wise multiply layer and
a sum layer, and add the unsupervised regularizer to further improve the domain
adaptation accuracy. In experiments for pedestrian detection, the proposed
method boosts the recall value by nearly 30% while the precision stays almost
the same. Furthermore, we perform our method on standard domain adaptation
benchmarks on both supervised and unsupervised settings and also achieve
state-of-the-art results.

6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th