Multilayer self-attention fusion pyramid network for enhanced image classification

Zixuan Wu; Yi Lin; Jiatong Hu; Jiaming Yang; Chiwen Feng

doi:10.1117/12.3050917

27 September 2024 Multilayer self-attention fusion pyramid network for enhanced image classification

Zixuan Wu, Yi Lin, Jiatong Hu, Jiaming Yang, Chiwen Feng

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328118 (2024) https://doi.org/10.1117/12.3050917
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

In recent years, deep learning has made remarkable strides, significantly propelling the field forward. However, traditional CNNs still face limitations in handling multi-scale information and capturing global features. To address these issues, we propose the Multi-Layer Self-Attention Fusion Pyramid Network (MSAPN). MSAPN combines ConvNeXt, feature pyramids, and self-attention modules to enhance the capability to integrate multiscale features and model global dependencies. Our network demonstrates superior performance across multiple public datasets, showcasing significant improvements in classification accuracy.

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

Citation Download Citation

Zixuan Wu, Yi Lin, Jiatong Hu, Jiaming Yang, and Chiwen Feng "Multilayer self-attention fusion pyramid network for enhanced image classification", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328118 (27 September 2024); https://doi.org/10.1117/12.3050917

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KEYWORDS

Image classification

Data modeling

Visual process modeling

Feature fusion

Transformers

Feature extraction

Image fusion

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