Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visualizing and Describing Fine-grained Categories as Textures   We analyze how categories from recent FGVC challenges can be described by
their textural content. The motivation is that subtle differences between
species of birds or butterflies can often be described in terms of the texture
associated with them and that several top-performing networks are inspired by
texture-based representations. These representations are characterized by
orderless pooling of second-order filter activations such as in bilinear CNNs
and the winner of the iNaturalist 2018 challenge. Concretely, for each category
we (i) visualize the "maximal images" by obtaining inputs x that maximize the
probability of the particular class according to a texture-based deep network,
and (ii) automatically describe the maximal images using a set of texture
attributes. The models for texture captioning were trained on our ongoing
efforts on collecting a dataset of describable textures building on the DTD
dataset. These visualizations indicate what aspects of the texture is most
discriminative for each category while the descriptions provide a
language-based explanation of the same.

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The categories of this paper are: econ
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an