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.

该论文采用决策级融合方法将文本和图像输入用于产品分类预测，通过训练深度神经网络和学习网络来选择输入类型，并成功地提高了大规模产品分类数据集上的准确性。