  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.
