Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards ontology driven learning of visual concept detectors   The maturity of deep learning techniques has led in recent years to a
breakthrough in object recognition in visual media. While for some specific
benchmarks, neural techniques seem to match if not outperform human judgement,
challenges are still open for detecting arbitrary concepts in arbitrary videos.
In this paper, we propose a system that combines neural techniques, a large
scale visual concepts ontology, and an active learning loop, to provide on the
fly model learning of arbitrary concepts. We give an overview of the system as
a whole, and focus on the central role of the ontology for guiding and
bootstrapping the learning of new concepts, improving the recall of concept
detection, and, on the user end, providing semantic search on a library of
annotated videos.

31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph