Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rich Image Captioning in the Wild   We present an image caption system that addresses new challenges of
automatically describing images in the wild. The challenges include high
quality caption quality with respect to human judgments, out-of-domain data
handling, and low latency required in many applications. Built on top of a
state-of-the-art framework, we developed a deep vision model that detects a
broad range of visual concepts, an entity recognition model that identifies
celebrities and landmarks, and a confidence model for the caption output.
Experimental results show that our caption engine outperforms previous
state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO)
and out of-domain datasets.

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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
9
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: quant-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci