Title
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce.
Abstract
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 from Walmart.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.
Year
Venue
Field
2016
national conference on artificial intelligence
Modalities,Data mining,Architecture,Computer science,Upload,Fusion,Artificial intelligence,Deep learning,Product classification,Machine learning,E-commerce,Modal
DocType
Volume
Citations 
Journal
abs/1611.09534
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Tom Zahavy100.34
Alessandro Magnani2114.17
Abhinandan Krishnan300.68
Shie Mannor43340285.45