Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entity-aware Image Caption Generation   Current image captioning approaches generate descriptions which lack specific
information, such as named entities that are involved in the images. In this
paper we propose a new task which aims to generate informative image captions,
given images and hashtags as input. We propose a simple but effective approach
to tackle this problem. We first train a convolutional neural networks - long
short term memory networks (CNN-LSTM) model to generate a template caption
based on the input image. Then we use a knowledge graph based collective
inference algorithm to fill in the template with specific named entities
retrieved via the hashtags. Experiments on a new benchmark dataset collected
from Flickr show that our model generates news-style image descriptions with
much richer information. Our model outperforms unimodal baselines significantly
with various evaluation metrics.

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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom