Knowledge Distillation for a Domain-Adaptive Visual Recommender System
In the last few years large-scale foundational models have shown remarkable performance in computer vision tasks. However, deploying such models in a production environment poses a significant challenge, because of their computational requirements. Furthermore, these models typically produce generic...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135533 |
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| _version_ | 1849736803328720896 |
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| author | Alessandro Abluton Luigi Portinale |
| author_facet | Alessandro Abluton Luigi Portinale |
| author_sort | Alessandro Abluton |
| collection | DOAJ |
| description | In the last few years large-scale foundational models have shown remarkable performance in computer vision tasks. However, deploying such models in a production environment poses a significant challenge, because of their computational requirements. Furthermore, these models typically produce generic results and they often need some sort of external input. The concept of knowledge distillation provides a promising solution to this problem.
In this paper, we focus on the challenges faced in the application of knowledge distillation techniques in the task of augmenting a dataset for object detection used in a commercial Visual Recommender System called VISIDEA; the goal consists in detecting items in various e-commerce websites, encompassing a wide range of custom product categories. We discuss a possible solution to problems such as label duplication, erroneous labeling and lack of robustness to prompting, by considering examples in the field of fashion apparel recommendation. |
| format | Article |
| id | doaj-art-14e8b1e800f14690aa0a9eee65ceb4ed |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-14e8b1e800f14690aa0a9eee65ceb4ed2025-08-20T03:07:10ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13553371912Knowledge Distillation for a Domain-Adaptive Visual Recommender SystemAlessandro Abluton0Luigi Portinale1https://orcid.org/0000-0002-6053-4667Inferendo srlUniversity of Eastern PiedmontIn the last few years large-scale foundational models have shown remarkable performance in computer vision tasks. However, deploying such models in a production environment poses a significant challenge, because of their computational requirements. Furthermore, these models typically produce generic results and they often need some sort of external input. The concept of knowledge distillation provides a promising solution to this problem. In this paper, we focus on the challenges faced in the application of knowledge distillation techniques in the task of augmenting a dataset for object detection used in a commercial Visual Recommender System called VISIDEA; the goal consists in detecting items in various e-commerce websites, encompassing a wide range of custom product categories. We discuss a possible solution to problems such as label duplication, erroneous labeling and lack of robustness to prompting, by considering examples in the field of fashion apparel recommendation.https://journals.flvc.org/FLAIRS/article/view/135533 |
| spellingShingle | Alessandro Abluton Luigi Portinale Knowledge Distillation for a Domain-Adaptive Visual Recommender System Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Knowledge Distillation for a Domain-Adaptive Visual Recommender System |
| title_full | Knowledge Distillation for a Domain-Adaptive Visual Recommender System |
| title_fullStr | Knowledge Distillation for a Domain-Adaptive Visual Recommender System |
| title_full_unstemmed | Knowledge Distillation for a Domain-Adaptive Visual Recommender System |
| title_short | Knowledge Distillation for a Domain-Adaptive Visual Recommender System |
| title_sort | knowledge distillation for a domain adaptive visual recommender system |
| url | https://journals.flvc.org/FLAIRS/article/view/135533 |
| work_keys_str_mv | AT alessandroabluton knowledgedistillationforadomainadaptivevisualrecommendersystem AT luigiportinale knowledgedistillationforadomainadaptivevisualrecommendersystem |