Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Predict: A Fast Re-constructive Method to Generate
  Multimodal Embeddings   Integrating visual and linguistic information into a single multimodal
representation is an unsolved problem with wide-reaching applications to both
natural language processing and computer vision. In this paper, we present a
simple method to build multimodal representations by learning a
language-to-vision mapping and using its output to build multimodal embeddings.
In this sense, our method provides a cognitively plausible way of building
representations, consistent with the inherently re-constructive and associative
nature of human memory. Using seven benchmark concept similarity tests we show
that the mapped vectors not only implicitly encode multimodal information, but
also outperform strong unimodal baselines and state-of-the-art multimodal
methods, thus exhibiting more "human-like" judgments---particularly in
zero-shot settings.

10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph