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Authors: Richárd Rádli ; Zsolt Vörösházi and László Czúni

Affiliation: University of Pannonia, 8200 Veszprém Egyetem u. 10., Hungary

Keyword(s): Metrics Learning, Pill Recognition, Self-Attention, Multihead Attention, Multi-Stream Network, Siamese Network, YOLO.

Abstract: In object recognition, especially, when new classes can easily appear during the application, few-shot learning has great importance. Metrics learning is an important elementary technique for few-shot object recognition which can be applied successfully for pill recognition. To enforce the exploitation of different object features we use multi-stream metrics learning networks for pill recognition in our article. We investigate the usage of multihead attention layers at different parts of the network. The performance is analyzed on two datasets with superior results to a state-of-the-art multi-stream pill recognition network.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Rádli, R. ; Vörösházi, Z. and Czúni, L. (2023). Pill Metrics Learning with Multihead Attention. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR; ISBN 978-989-758-671-2; ISSN 2184-3228, SciTePress, pages 132-140. DOI: 10.5220/0012235500003598

@conference{kdir23,
author={Richárd Rádli and Zsolt Vörösházi and László Czúni},
title={Pill Metrics Learning with Multihead Attention},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR},
year={2023},
pages={132-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012235500003598},
isbn={978-989-758-671-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR
TI - Pill Metrics Learning with Multihead Attention
SN - 978-989-758-671-2
IS - 2184-3228
AU - Rádli, R.
AU - Vörösházi, Z.
AU - Czúni, L.
PY - 2023
SP - 132
EP - 140
DO - 10.5220/0012235500003598
PB - SciTePress

- Science and Technology Publications, Lda.
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