# Multimodal E-Commerce Product Classification Using Hierarchical Fusion

arXiv: https://arxiv.org/abs/2207.03305 · HTML: https://mlsanity.com/paper/2207.03305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions](https://mlsanity.com/paper/1612.05386/) (2016)
- [A Multimodal Late Fusion Model for E-Commerce Product Classification](https://mlsanity.com/paper/2008.06179/) (2020)
- [Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-Attention](https://mlsanity.com/paper/2011.11735/) (2020)

## Built on by (most cited first, 1 of 1)

- [Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach](https://mlsanity.com/paper/2508.20013/) (2025)

## Abstract (arXiv)

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (CamemBERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the unimodal models' performance and the reported performance of similar models on our specific task. We did experiments with multiple fusing techniques and found, that the best performing technique to combine the individual embedding of the unimodal network is based on combining concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems.