Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cooperative Embeddings for Instance, Attribute and Category Retrieval   The goal of this paper is to retrieve an image based on instance, attribute
and category similarity notions. Different from existing works, which usually
address only one of these entities in isolation, we introduce a cooperative
embedding to integrate them while preserving their specific level of semantic
representation. An algebraic structure defines a superspace filled with
instances. Attributes are axis-aligned to form subspaces, while categories
influence the arrangement of similar instances. These relationships enable them
to cooperate for their mutual benefits for image retrieval. We derive a
proxy-based softmax embedding loss to learn simultaneously all similarity
measures in both superspace and subspaces. We evaluate our model on datasets
from two different domains. Experiments on image retrieval tasks show the
benefits of the cooperative embeddings for modeling multiple image
similarities, and for discovering style evolution of instances between- and
within-categories.

18
The categories of this paper are: econ
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol