Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Question Answering as a Multi-Task Problem   Visual Question Answering(VQA) is a highly complex problem set, relying on
many sub-problems to produce reasonable answers. In this paper, we present the
hypothesis that Visual Question Answering should be viewed as a multi-task
problem, and provide evidence to support this hypothesis. We demonstrate this
by reformatting two commonly used Visual Question Answering datasets, COCO-QA
and DAQUAR, into a multi-task format and train these reformatted datasets on
two baseline networks, with one designed specifically to eliminate other
possible causes for performance changes as a result of the reformatting. Though
the networks demonstrated in this paper do not achieve strongly competitive
results, we find that the multi-task approach to Visual Question Answering
results in increases in performance of 5-9% against the single-task formatting,
and that the networks reach convergence much faster than in the single-task
case. Finally we discuss possible reasons for the observed difference in
performance, and perform additional experiments which rule out causes not
associated with the learning of the dataset as a multi-task problem.

15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga