Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Information Extraction from Images with Learned Semantic
  Models   Many applications require an understanding of an image that goes beyond the
simple detection and classification of its objects. In particular, a great deal
of semantic information is carried in the relationships between objects. We
have previously shown that the combination of a visual model and a statistical
semantic prior model can improve on the task of mapping images to their
associated scene description. In this paper, we review the model and compare it
to a novel conditional multi-way model for visual relationship detection, which
does not include an explicitly trained visual prior model. We also discuss
potential relationships between the proposed methods and memory models of the
human brain.

34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin