Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning with Average Precision: Training Image Retrieval with a
  Listwise Loss   Image retrieval can be formulated as a ranking problem where the goal is to
order database images by decreasing similarity to the query. Recent deep models
for image retrieval have outperformed traditional methods by leveraging
ranking-tailored loss functions, but important theoretical and practical
problems remain. First, rather than directly optimizing the global ranking,
they minimize an upper-bound on the essential loss, which does not necessarily
result in an optimal mean average precision (mAP). Second, these methods
require significant engineering efforts to work well, e.g. special pre-training
and hard-negative mining. In this paper we propose instead to directly optimize
the global mAP by leveraging recent advances in listwise loss formulations.
Using a histogram binning approximation, the AP can be differentiated and thus
employed to end-to-end learning. Compared to existing losses, the proposed
method considers thousands of images simultaneously at each iteration and
eliminates the need for ad hoc tricks. It also establishes a new state of the
art on many standard retrieval benchmarks. Models and evaluation scripts have
been made available at https://europe.naverlabs.com/Deep-Image-Retrieval/

31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: astro-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