Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Addressing Training Bias via Automated Image Annotation   Build accurate DNN models requires training on large labeled, context
specific datasets, especially those matching the target scenario. We believe
advances in wireless localization, working in unison with cameras, can produce
automated annotation of targets on images and videos captured in the wild.
Using pedestrian and vehicle detection as examples, we demonstrate the
feasibility, benefits, and challenges of an automatic image annotation system.
Our work calls for new technical development on passive localization, mobile
data analytics, and error-resilient ML models, as well as design issues in user
privacy policies.

7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
18
The categories of this paper are: econ