Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaud\'i: Conversational Interactions with Deep Representations to
  Generate Image Collections   Based on recent advances in realistic language modeling (GPT-3) and
cross-modal representations (CLIP), Gaud\'i was developed to help designers
search for inspirational images using natural language. In the early stages of
the design process, with the goal of eliciting a client's preferred creative
direction, designers will typically create thematic collections of
inspirational images called "mood-boards". Creating a mood-board involves
sequential image searches which are currently performed using keywords or
images. Gaud\'i transforms this process into a conversation where the user is
gradually detailing the mood-board's theme. This representation allows our AI
to generate new search queries from scratch, straight from a project briefing,
following a theme hypothesized by GPT-3. Compared to previous computational
approaches to mood-board creation, to the best of our knowledge, ours is the
first attempt to represent mood-boards as the stories that designers tell when
presenting a creative direction to a client.

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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga