Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Image Captioning with Attributes   Automatically describing an image with a natural language has been an
emerging challenge in both fields of computer vision and natural language
processing. In this paper, we present Long Short-Term Memory with Attributes
(LSTM-A) - a novel architecture that integrates attributes into the successful
Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs)
image captioning framework, by training them in an end-to-end manner. To
incorporate attributes, we construct variants of architectures by feeding image
representations and attributes into RNNs in different ways to explore the
mutual but also fuzzy relationship between them. Extensive experiments are
conducted on COCO image captioning dataset and our framework achieves superior
results when compared to state-of-the-art deep models. Most remarkably, we
obtain METEOR/CIDEr-D of 25.2%/98.6% on testing data of widely used and
publicly available splits in (Karpathy & Fei-Fei, 2015) when extracting image
representations by GoogleNet and achieve to date top-1 performance on COCO
captioning Leaderboard.

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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex