Abstract
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.
Lingua originale | Inglese |
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Stato di pubblicazione | Pubblicato - 2024 |
Evento | FLAIRS-37 - Miramar beach, FL Durata: 1 gen 2024 → … |
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???event.eventtypes.event.conference??? | FLAIRS-37 |
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Città | Miramar beach, FL |
Periodo | 1/01/24 → … |
Keywords
- Visual recommendation
- deep learning
- recommender systems