2 research outputs found
Multimodal Side-Tuning for Document Classification
In this paper, we propose to exploit the side-tuning framework for multimodal
document classification. Side-tuning is a methodology for network adaptation
recently introduced to solve some of the problems related to previous
approaches. Thanks to this technique it is actually possible to overcome model
rigidity and catastrophic forgetting of transfer learning by fine-tuning. The
proposed solution uses off-the-shelf deep learning architectures leveraging the
side-tuning framework to combine a base model with a tandem of two side
networks. We show that side-tuning can be successfully employed also when
different data sources are considered, e.g. text and images in document
classification. The experimental results show that this approach pushes further
the limit for document classification accuracy with respect to the state of the
art.Comment: 2020 25th International Conference on Pattern Recognition (ICPR