Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Is a picture worth a thousand words? A Deep Multi-Modal Fusion
  Architecture for Product Classification in e-commerce   Classifying products into categories precisely and efficiently is a major
challenge in modern e-commerce. The high traffic of new products uploaded daily
and the dynamic nature of the categories raise the need for machine learning
models that can reduce the cost and time of human editors. In this paper, we
propose a decision level fusion approach for multi-modal product classification
using text and image inputs. We train input specific state-of-the-art deep
neural networks for each input source, show the potential of forging them
together into a multi-modal architecture and train a novel policy network that
learns to choose between them. Finally, we demonstrate that our multi-modal
network improves the top-1 accuracy % over both networks on a real-world
large-scale product classification dataset that we collected fromWalmart.com.
While we focus on image-text fusion that characterizes e-commerce domains, our
algorithms can be easily applied to other modalities such as audio, video,
physical sensors, etc.

31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom