Transformer based fine-grained recognition algorithm via part selecting

Biyu Song; Wei Wu

doi:10.1117/12.3035374

19 July 2024 Transformer based fine-grained recognition algorithm via part selecting

Biyu Song, Wei Wu

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131P (2024) https://doi.org/10.1117/12.3035374
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Fine-grained image classification has the characteristic of large inter-class differences and small intra-class differences. The challenge lies in the fact that different subclasses have similar structures and minimal differences. The key to distinguishing different subclasses lies in differentiating certain components of the image subject, such as the beak, claws, and tail of a bird, and there is also a certain relationship between these components. With the application of Transformer in visual processing, researchers have discovered the inherent advantages of Transformer in establishing dependencies. This paper fully considers the characteristics of Transformer in mining contextual relationships and proposes a fine-grained image classification algorithm based on Transformer and patch-selection mechanism for extracting component features.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Biyu Song and Wei Wu "Transformer based fine-grained recognition algorithm via part selecting", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131P (19 July 2024); https://doi.org/10.1117/12.3035374

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KEYWORDS

Transformers

Image classification

Correlation coefficients

Feature extraction

Detection and tracking algorithms

Education and training

Performance modeling

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