Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912354629844992 |
|---|---|
| author | Boyadzhiev, Teodor Lagani, Gabriele Ciampi, Luca Amato, Giuseppe Ivanova, Krassimira |
| author_facet | Boyadzhiev, Teodor Lagani, Gabriele Ciampi, Luca Amato, Giuseppe Ivanova, Krassimira |
| contents | The integration of computer vision and deep learning is an essential part of documenting and preserving cultural heritage, as well as improving visitor experiences. In recent years, two deep learning paradigms have been established in the field of computer vision: convolutional neural networks and transformer architectures. The present study aims to make a comparative analysis of some representatives of these two techniques of their ability to transfer knowledge from generic dataset, such as ImageNet, to cultural heritage specific tasks. The results of testing examples of the architectures VGG, ResNet, DenseNet, Visual Transformer, Swin Transformer, and PoolFormer, showed that DenseNet is the best in terms of efficiency-computability ratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain Boyadzhiev, Teodor Lagani, Gabriele Ciampi, Luca Amato, Giuseppe Ivanova, Krassimira Computer Vision and Pattern Recognition The integration of computer vision and deep learning is an essential part of documenting and preserving cultural heritage, as well as improving visitor experiences. In recent years, two deep learning paradigms have been established in the field of computer vision: convolutional neural networks and transformer architectures. The present study aims to make a comparative analysis of some representatives of these two techniques of their ability to transfer knowledge from generic dataset, such as ImageNet, to cultural heritage specific tasks. The results of testing examples of the architectures VGG, ResNet, DenseNet, Visual Transformer, Swin Transformer, and PoolFormer, showed that DenseNet is the best in terms of efficiency-computability ratio. |
| title | Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.21387 |