Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CurlingNet: Compositional Learning between Images and Text for Fashion
  IQ Data   We present an approach named CurlingNet that can measure the semantic
distance of composition of image-text embedding. In order to learn an effective
image-text composition for the data in the fashion domain, our model proposes
two key components as follows. First, the Delivery makes the transition of a
source image in an embedding space. Second, the Sweeping emphasizes
query-related components of fashion images in the embedding space. We utilize a
channel-wise gating mechanism to make it possible. Our single model outperforms
previous state-of-the-art image-text composition models including TIRG and
FiLM. We participate in the first fashion-IQ challenge in ICCV 2019, for which
ensemble of our model achieves one of the best performances.

0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg