{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multimodal-side-tuning-for-document","title":"Multimodal Side-Tuning for Document Classification","arxiv_id":"2301.07502","date":"2023-01-16","proceeding":null,"authors":["Stefano Pio Zingaro","Giuseppe Lisanti","Maurizio Gabbrielli"],"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.","url_abs":"https://arxiv.org/abs/2301.07502v2","url_pdf":"https://arxiv.org/pdf/2301.07502v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multimodal-side-tuning-for-document","repo_url":"https://github.com/thezingaro/multimodal-side-tuning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"document-image-classification","task_name":"Document Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"fasttext","method_name":"fastText"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/document-image-classification-on-rvl-cdip","task":"Document Image Classification","dataset":"RVL-CDIP","model":"Multimodal (ResNet50)","rank_in_archive_order":21,"of":31,"metrics":{"Accuracy":"92.7%","Parameters":"57M"},"uses_additional_data":false},{"leaderboard":"/sota/document-image-classification-on-rvl-cdip","task":"Document Image Classification","dataset":"RVL-CDIP","model":"Multimodal (MobileNetV2)","rank_in_archive_order":25,"of":31,"metrics":{"Accuracy":"92.2%","Parameters":"12M"},"uses_additional_data":false},{"leaderboard":"/sota/document-image-classification-on-tobacco-3482","task":"Document Image Classification","dataset":"Tobacco-3482","model":"Multimodal Side-Tuning (MobileNetV2)","rank_in_archive_order":4,"of":10,"metrics":{"Accuracy":"90.50"},"uses_additional_data":false},{"leaderboard":"/sota/document-image-classification-on-tobacco-3482","task":"Document Image Classification","dataset":"Tobacco-3482","model":"Multimodal Side-Tuning (ResNet50)","rank_in_archive_order":5,"of":10,"metrics":{"Accuracy":"90.30"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}