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.

Stefano Pio Zingaro, G.L. (2021). Multimodal Side-Tuning for Document Classification [10.1109/ICPR48806.2021.9413208].

Multimodal Side-Tuning for Document Classification

Stefano Pio Zingaro
Primo
Methodology
;
Giuseppe Lisanti
Secondo
Conceptualization
;
Maurizio Gabbrielli
Ultimo
Supervision
2021

Abstract

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.
2021
2020 25th International Conference on Pattern Recognition (ICPR)
5206
5213
Stefano Pio Zingaro, G.L. (2021). Multimodal Side-Tuning for Document Classification [10.1109/ICPR48806.2021.9413208].
Stefano Pio Zingaro, Giuseppe Lisanti, Maurizio Gabbrielli
File in questo prodotto:
File Dimensione Formato  
Multimodal Side- Tuning for Document Classification.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 2.13 MB
Formato Adobe PDF
2.13 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/789779
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 2
social impact