Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Question Answering with Memory-Augmented Networks   In this paper, we exploit a memory-augmented neural network to predict
accurate answers to visual questions, even when those answers occur rarely in
the training set. The memory network incorporates both internal and external
memory blocks and selectively pays attention to each training exemplar. We show
that memory-augmented neural networks are able to maintain a relatively
long-term memory of scarce training exemplars, which is important for visual
question answering due to the heavy-tailed distribution of answers in a general
VQA setting. Experimental results on two large-scale benchmark datasets show
the favorable performance of the proposed algorithm with a comparison to state
of the art.

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The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
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The categories of this paper are: econ
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con