Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding beans in burgers: Deep semantic-visual embedding with
  localization   Several works have proposed to learn a two-path neural network that maps
images and texts, respectively, to a same shared Euclidean space where geometry
captures useful semantic relationships. Such a multi-modal embedding can be
trained and used for various tasks, notably image captioning. In the present
work, we introduce a new architecture of this type, with a visual path that
leverages recent space-aware pooling mechanisms. Combined with a textual path
which is jointly trained from scratch, our semantic-visual embedding offers a
versatile model. Once trained under the supervision of captioned images, it
yields new state-of-the-art performance on cross-modal retrieval. It also
allows the localization of new concepts from the embedding space into any input
image, delivering state-of-the-art result on the visual grounding of phrases.

18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph