2016/11/29 by Tom Zahavy, Alessandro Magnani, Zahavy, Tom +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.1611.09534
openalex publication_date 2016/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.