# A Multimodal Late Fusion Model for E-Commerce Product Classification

arXiv: https://arxiv.org/abs/2008.06179 · HTML: https://mlsanity.com/paper/2008.06179/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Multimodal E-Commerce Product Classification Using Hierarchical Fusion](https://mlsanity.com/paper/2207.03305/) (2022)
- [Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach](https://mlsanity.com/paper/2508.20013/) (2025)
- [Rites de Passage: Elucidating Displacement to Emplacement of Refugees on Twitter](https://mlsanity.com/paper/2206.03248/) (2022)

## Abstract (arXiv)

The cataloging of product listings is a fundamental problem for most e-commerce platforms. Despite promising results obtained by unimodal-based methods, it can be expected that their performance can be further boosted by the consideration of multimodal product information. In this study, we investigated a multimodal late fusion approach based on text and image modalities to categorize e-commerce products on Rakuten. Specifically, we developed modal specific state-of-the-art deep neural networks for each input modal, and then fused them at the decision level. Experimental results on Multimodal Product Classification Task of SIGIR 2020 E-Commerce Workshop Data Challenge demonstrate the superiority and effectiveness of our proposed method compared with unimodal and other multimodal methods. Our team named pa_curis won the 1st place with a macro-F1 of 0.9144 on the final leaderboard.