Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Location Sensitive Image Retrieval and Tagging   People from different parts of the globe describe objects and concepts in
distinct manners. Visual appearance can thus vary across different geographic
locations, which makes location a relevant contextual information when
analysing visual data. In this work, we address the task of image retrieval
related to a given tag conditioned on a certain location on Earth. We present
LocSens, a model that learns to rank triplets of images, tags and coordinates
by plausibility, and two training strategies to balance the location influence
in the final ranking. LocSens learns to fuse textual and location information
of multimodal queries to retrieve related images at different levels of
location granularity, and successfully utilizes location information to improve
image tagging.

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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an