Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Relational Memory Network for Unsupervised Image Captioning   Unsupervised image captioning with no annotations is an emerging challenge in
computer vision, where the existing arts usually adopt GAN (Generative
Adversarial Networks) models. In this paper, we propose a novel memory-based
network rather than GAN, named Recurrent Relational Memory Network ($R^2M$).
Unlike complicated and sensitive adversarial learning that non-ideally performs
for long sentence generation, $R^2M$ implements a concepts-to-sentence memory
translator through two-stage memory mechanisms: fusion and recurrent memories,
correlating the relational reasoning between common visual concepts and the
generated words for long periods. $R^2M$ encodes visual context through
unsupervised training on images, while enabling the memory to learn from
irrelevant textual corpus via supervised fashion. Our solution enjoys less
learnable parameters and higher computational efficiency than GAN-based
methods, which heavily bear parameter sensitivity. We experimentally validate
the superiority of $R^2M$ than state-of-the-arts on all benchmark datasets.

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The categories of this paper are: astro-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: stat
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The categories of this paper are: nucl-th