Research on AI Small Sample Image Recognition

Jixin Chen, Songrong Lv

2024

Abstract

In computer vision and image recognition technology, as the input resolution changes, the recognition effect of convolutional neural network methods also varies. Meta learning allows computers to simulate the human brain and learn how to learn, which can achieve image classification more efficiently and flexibly. this paper will first focus on technical progress, application field expansion and challenge and response of AI image recognition. This article introduces image classification based on meta learning, followed by image classification based on data augmentation and image grading based on transfer learning, Finally, a summary and outlook on small sample image recognition technology were presented. With the continuous develop of deep learning, transfer learning, meta learning and other technology, AI small sample image recognition models will be more optimized and able to achieve higher recognition accuracy with less data. The technology will play an important role in more fields and bring more convenience and value to mankind.

Paper Citation

in Harvard Style

Chen J. and Lv S. (2024). Research on AI Small Sample Image Recognition. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 474-479. DOI: 10.5220/0013422200004558

in Bibtex Style

@conference{mlscm24,
author={Jixin Chen and Songrong Lv},
title={Research on AI Small Sample Image Recognition},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={474-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013422200004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Research on AI Small Sample Image Recognition
SN - 978-989-758-738-2
AU - Chen J.
AU - Lv S.
PY - 2024
SP - 474
EP - 479
DO - 10.5220/0013422200004558
PB - SciTePress