Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly Supervised One-Shot Detection with Attention Similarity Networks   Neural network models that are not conditioned on class identities were shown
to facilitate knowledge transfer between classes and to be well-suited for
one-shot learning tasks. Following this motivation, we further explore and
establish such models and present a novel neural network architecture for the
task of weakly supervised one-shot detection. Our model is only conditioned on
a single exemplar of an unseen class and a larger target example that may or
may not contain an instance of the same class as the exemplar. By pairing a
Siamese similarity network with an attention mechanism, we design a model that
manages to simultaneously identify and localise instances of classes unseen at
training time. In experiments with datasets from the computer vision and audio
domains, the proposed method considerably outperforms the baseline methods for
the weakly supervised one-shot detection task.

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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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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: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn