Version 1
: Received: 5 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (12:19:19 CEST)
How to cite:
Guo, Y.; Li, B.; Zhang, W.; Dong, W. Local Structure Information Learning for Small Dataset Fine-Grained Visual Classification Based on Spatial Frequency Domain Fusion. Preprints2024, 2024090630. https://doi.org/10.20944/preprints202409.0630.v1
Guo, Y.; Li, B.; Zhang, W.; Dong, W. Local Structure Information Learning for Small Dataset Fine-Grained Visual Classification Based on Spatial Frequency Domain Fusion. Preprints 2024, 2024090630. https://doi.org/10.20944/preprints202409.0630.v1
Guo, Y.; Li, B.; Zhang, W.; Dong, W. Local Structure Information Learning for Small Dataset Fine-Grained Visual Classification Based on Spatial Frequency Domain Fusion. Preprints2024, 2024090630. https://doi.org/10.20944/preprints202409.0630.v1
APA Style
Guo, Y., Li, B., Zhang, W., & Dong, W. (2024). Local Structure Information Learning for Small Dataset Fine-Grained Visual Classification Based on Spatial Frequency Domain Fusion. Preprints. https://doi.org/10.20944/preprints202409.0630.v1
Chicago/Turabian Style
Guo, Y., Wenyue Zhang and Weilong Dong. 2024 "Local Structure Information Learning for Small Dataset Fine-Grained Visual Classification Based on Spatial Frequency Domain Fusion" Preprints. https://doi.org/10.20944/preprints202409.0630.v1
Abstract
In small data set learning, obtaining a large number of training samples from the source class for transfer learning is a challenge for fine-grained visual classification. Based on the fact that fine-grained concepts can be learned with very few samples, we use very few labeled samples (for example, three) in each category. However, due to the difficulty in distinguishing the subtle differences between fine-grained images, we propose a local structure information extraction method of SDFGVC based on spatial frequency features. The proposed learning module enhances the ability of the network to find significant regions and shows excellent performance in experiments on six datasets.
Keywords
Local structure information(LSI); spatial frequency domain features(SFD); fine-grained visual classification (FGVC); small dataset fine-grained visual classification (SDFGVC)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
