Multimodal Side-Tuning for Document Classification
This work addresses document classification for multimodal data, but it is incremental as it extends an existing framework to a new application.
The paper tackled multimodal document classification by applying the side-tuning framework to combine text and image data, achieving improved accuracy over state-of-the-art methods.
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.