Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings
  Using Abstract Scenes   We propose a model to learn visually grounded word embeddings (vis-w2v) to
capture visual notions of semantic relatedness. While word embeddings trained
using text have been extremely successful, they cannot uncover notions of
semantic relatedness implicit in our visual world. For instance, although
"eats" and "stares at" seem unrelated in text, they share semantics visually.
When people are eating something, they also tend to stare at the food.
Grounding diverse relations like "eats" and "stares at" into vision remains
challenging, despite recent progress in vision. We note that the visual
grounding of words depends on semantics, and not the literal pixels. We thus
use abstract scenes created from clipart to provide the visual grounding. We
find that the embeddings we learn capture fine-grained, visually grounded
notions of semantic relatedness. We show improvements over text-only word
embeddings (word2vec) on three tasks: common-sense assertion classification,
visual paraphrasing and text-based image retrieval. Our code and datasets are
available online.

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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph