Main Article Content
The accuracy of small-sample image classification depends on the ability of neural network models to extract image representations from sample data. A small-sample image classification system based on attention mechanisms and meta-learning is suggested in order to extract more comprehensive information from pictures. The approach may extract richer multiscale features from pictures and enhance classification outcomes through meta-learning because the attention mechanism of multiscale features can concentrate on the data in the sample feature space. In order to demonstrate the efficacy of the suggested strategy, tests are conducted on the two industry-standard datasets miniImageNet and tieredImageNet for both 5-way 1-shot and 5-way 5-shot tasks. The results are compared with the best existing methods.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to• Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.