Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MAttNet: Modular Attention Network for Referring Expression
  Comprehension   In this paper, we address referring expression comprehension: localizing an
image region described by a natural language expression. While most recent work
treats expressions as a single unit, we propose to decompose them into three
modular components related to subject appearance, location, and relationship to
other objects. This allows us to flexibly adapt to expressions containing
different types of information in an end-to-end framework. In our model, which
we call the Modular Attention Network (MAttNet), two types of attention are
utilized: language-based attention that learns the module weights as well as
the word/phrase attention that each module should focus on; and visual
attention that allows the subject and relationship modules to focus on relevant
image components. Module weights combine scores from all three modules
dynamically to output an overall score. Experiments show that MAttNet
outperforms previous state-of-art methods by a large margin on both
bounding-box-level and pixel-level comprehension tasks. Demo and code are
provided.

22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph