Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: M2FN: Multi-step Modality Fusion for Advertisement Image Assessment   Assessing advertisements, specifically on the basis of user preferences and
ad quality, is crucial to the marketing industry. Although recent studies have
attempted to use deep neural networks for this purpose, these studies have not
utilized image-related auxiliary attributes, which include embedded text
frequently found in ad images. We, therefore, investigated the influence of
these attributes on ad image preferences. First, we analyzed large-scale
real-world ad log data and, based on our findings, proposed a novel multi-step
modality fusion network (M2FN) that determines advertising images likely to
appeal to user preferences. Our method utilizes auxiliary attributes through
multiple steps in the network, which include conditional batch
normalization-based low-level fusion and attention-based high-level fusion. We
verified M2FN on the AVA dataset, which is widely used for aesthetic image
assessment, and then demonstrated that M2FN can achieve state-of-the-art
performance in preference prediction using a real-world ad dataset with rich
auxiliary attributes.

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The categories of this paper are: math
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
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The categories of this paper are: physics
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio