Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Textures in Zero-shot Understanding of Fine-Grained Domains   Textures can be used to describe the appearance of objects in a wide range of
fine-grained domains. Textures are localized and one can often refer to their
properties in a manner that is independent of the object identity. Moreover,
there is a rich vocabulary to describe textures corresponding to properties
such as their color, pattern, structure, periodicity, stochasticity, and
others. Motivated by this, we study the effectiveness of large-scale language
and vision models (e.g., CLIP) at recognizing texture attributes in natural
images. We first conduct a systematic study of CLIP on texture datasets where
we find that it has good coverage for a wide range of texture terms. CLIP can
also handle compositional phrases that consist of color and pattern terms
(e.g., red dots or yellow stripes). We then show how these attributes allow for
zero-shot fine-grained categorization on existing datasets.

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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat