RT info:eu-repo/semantics/article
T1 One Shot Learning with class partitioning and cross validation voting (CP-CVV)
A1 Duque Domingo, Jaime
A1 Medina Aparicio, Roberto
A1 González Rodrigo, Luis Miguel
K1 Artificial intelligence
K1 Image Processing and Computer Vision
K1 One shot learning
K1 Grocery Product Classification
K1 Wide ResNet
K1 Clasificación de productos de alimentación
K1 ResNet amplia
K1 33 Ciencias Tecnológicas
AB One Shot Learning includes all those techniques that make it possible to classify images using a single image per category. One of its possible applications is the identification of food products. For a grocery store, it is interesting to record a single image of each product and be able to recognise it again from other images, such as photos taken by customers. Within deep learning, Siamese neural networks are able to verify whether two images belong to the same category or not. In this paper, a new Siamese network training technique, called CP-CVV, is presented. It uses the combination of different models trained with different classes. The separation of validation classes has been done in such a way that each of the combined models is different in order to avoid overfitting with respect to the validation. Unlike normal training, the test images belong to classes that have not previously been used in training, allowing the model to work on new categories, of which only one image exists. Different backbones have been evaluated in the Siamese composition, but also the integration of multiple models with different backbones. The results show that the model improves on previous works and allows the classification problem to be solved, an additional step towards the use of Siamese networks. To the best of our knowledge, there is no existing work that has proposed integrating Siamese neural networks using a class-based validation set separation technique so as to be better at generalising for unknown classes. Additionally, we have applied Cross-Validation-Voting with ConvNeXt to improve the existing classification results of a well-known Grocery Store Dataset.
PB Elsevier
SN 0031-3203
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/60065
UL https://uvadoc.uva.es/handle/10324/60065
LA eng
NO Pattern Recognition, 2023, vol. 143, 109797
NO Producción Científica
DS UVaDOC
RD 22-dic-2024