Version 1
: Received: 18 September 2024 / Approved: 18 September 2024 / Online: 20 September 2024 (03:47:37 CEST)
How to cite:
Rao, J.; Qin, S.; An, Z.; Zhang, J.; Bao, Q.; Peng, Z. Feature Coding and Graph via Transformer: Different Granularities Classification for Aircrafts. Preprints2024, 2024091433. https://doi.org/10.20944/preprints202409.1433.v1
Rao, J.; Qin, S.; An, Z.; Zhang, J.; Bao, Q.; Peng, Z. Feature Coding and Graph via Transformer: Different Granularities Classification for Aircrafts. Preprints 2024, 2024091433. https://doi.org/10.20944/preprints202409.1433.v1
Rao, J.; Qin, S.; An, Z.; Zhang, J.; Bao, Q.; Peng, Z. Feature Coding and Graph via Transformer: Different Granularities Classification for Aircrafts. Preprints2024, 2024091433. https://doi.org/10.20944/preprints202409.1433.v1
APA Style
Rao, J., Qin, S., An, Z., Zhang, J., Bao, Q., & Peng, Z. (2024). Feature Coding and Graph via Transformer: Different Granularities Classification for Aircrafts. Preprints. https://doi.org/10.20944/preprints202409.1433.v1
Chicago/Turabian Style
Rao, J., Qiliang Bao and Zhenming Peng. 2024 "Feature Coding and Graph via Transformer: Different Granularities Classification for Aircrafts" Preprints. https://doi.org/10.20944/preprints202409.1433.v1
Abstract
Against the background of the sky, imaging and perception of aircraft are cru-
cial. Among these, the location of aircrafts is not a very difficult problem
in general, because that deep learning algorithms and object detection models
based on convolutional neural network(CNN) are ever-evolving. In order to have
a better identification of various aircrafts, or to accurately locate different types
of flying targets in the sky, it becomes significant to distinguish and recognize
different types of aircrafts. One question is that, distinguishing and recognizing
between sub-categories of aircrafts pose great challenges. Although fine-grained
recognition focuses on exploring and studying such problems, aircrafts under
different sub-categories and granularities lead us to rethink the application of
features. We noticed that features in swin-transformer demonstrates the under-
standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a
better understanding of features encoding and use, which are inspired by the
feature encoding and computation in swin-transformer. In our paper, our ap-
proach has achieved effects on features encoded graphically, manifested from the
architecture design and convolutional neural network computation, and outper-
forms other famous fine-graiend classification models on this issue. Not only
approach we proposed has demonstrated superior performance in fine-grained
aicraft classification, but also the mechanisms of the feature encoding under
different sample space partitions is revealed for this issue, which provides a re-
search for the representation of aircraft fineness characteristics. The relationship
between representation orientation of aircraft feature information under various
grained divisions, shows that the recognition of different categories according to
man-made, such as aircraft, can be achieved by means of the feature represen-
tations studied in this paper, for more specific defined classification tasks and
various man-made targets partition criteria, which may influences the princi-
ple of design for calculation and feature extraction in fine-grained classification
models.
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.
