Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Answer Questions From Image Using Convolutional Neural
  Network   In this paper, we propose to employ the convolutional neural network (CNN)
for the image question answering (QA). Our proposed CNN provides an end-to-end
framework with convolutional architectures for learning not only the image and
question representations, but also their inter-modal interactions to produce
the answer. More specifically, our model consists of three CNNs: one image CNN
to encode the image content, one sentence CNN to compose the words of the
question, and one multimodal convolution layer to learn their joint
representation for the classification in the space of candidate answer words.
We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA
datasets, which are two benchmark datasets for the image QA, with the
performances significantly outperforming the state-of-the-art.

15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex