Reda Khoufache, M. Dilmi, Hanene Azzag, Etienne Gofinnet, M. Lebbah
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引用次数: 0
Abstract
Artificial Intelligence (AI) in supermarkets is moving fast with the recent advances in deep learning. One important project in the retail sector is the development of AI solutions for smart stores, mainly to improve product recognition. In this paper, we present a new framework to address the multi-view image classification using multiple clustering. The proposed framework combines a pre-trained Vision Transformer with a Bayesian Non-Parametric multiple clustering. In this work, we propose an M CM C- based inference approach to learn the column-partition and the row-partitions. This method infers multiple clustering solutions and allows to find automatically the number of clusters. Our method provides interesting results on a multi-view image dataset and emphasizes, on one hand, the power of pre-trained Vision Transformers combined with the multiple clustering algorithm, on the other hand, the usefulness of the Bayesian Non-Parametric modeling, which automatically performs a model selection.
随着深度学习的最新进展,超市中的人工智能(AI)正在迅速发展。零售领域的一个重要项目是为智能商店开发人工智能解决方案,主要是为了提高产品识别。本文提出了一种新的基于多聚类的多视图图像分类框架。该框架将预训练的视觉转换器与贝叶斯非参数多聚类相结合。在这项工作中,我们提出了一种基于M - CM - C的推理方法来学习列分区和行分区。该方法推断出多个聚类解决方案,并允许自动查找聚类的数量。我们的方法在多视图图像数据集上提供了有趣的结果,并且一方面强调了预先训练的视觉变形器与多聚类算法相结合的强大功能,另一方面强调了贝叶斯非参数建模的有用性,该建模可以自动执行模型选择。